{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 简介\n",
    "决策树(decision tree)是一种监督学习算法，广泛应用与分类和回归人物。它通过构建一颗树形结构来进行预测，每个内部节点表示一个属性上的测试点，每个分支表示一个测试结果，而每个节点保存一个类别标签或数值输出。决策树具有易于理解和解释的优点，可以处理数值型和分类型数据，并且不需要对数据进行标准化。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 决策书的基本概念\n",
    "* 根节点(Root Node) : 没有输入边，是所有路径的起点。\n",
    "* 内部节点（Interior Node）：包含一个或多个边，用于测试某个属性。\n",
    "* 叶节点（Leaf Node）：没有任何输出边，持有最终的预测值。\n",
    "* 分支（Branch）：从一个节点到另外一个节点的路径，代表某种条件下的结果。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 构建决策树的基本过程\n",
    "* 选择最佳分割属性：在每个节点处，根据一定的标准（如信息增益、基尼系数等）选择最优的属性来划分数据集。目的是找到能够最大程度地区分不同类别的属性。\n",
    "* 递归地创建子树：对于每个可能的属性值，生成一个新的子节点，并将相应的数据子集分配给该节点。然后对这些子节点重复上述过程，直到满足停止条件（例如，所有的样本都属于同一类别，或者没有更多的属性可用于分裂）。\n",
    "* 剪枝（Pruning）：为了避免过拟合，可以通过剪去一些不必要的分支来简化模型。剪枝分为预剪枝（在树生长过程中提前终止）和后剪枝（先生成完整的树再去除不重要的部分）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 常见的决策树算法\n",
    "* ID3 (Iterative Dichotomiser 3)：最早期的决策树算法之一，使用信息增益作为衡量标准来选择分割属性。仅能处理离散型数据。\n",
    "* C4.5：ID3的改进版本，支持连续型数据，并引入了信息增益比来克服信息增益偏向于多值属性的问题。此外，C4.5还实现了后剪枝。\n",
    "* CART (Classification and Regression Trees)：既可以做分类也可以做回归。对于分类问题，CART采用基尼指数作为评估指标；对于回归问题，则使用方差减少量。CART总是生成二叉树，并且也包含了剪枝机制。\n",
    "CHAID (Chi-squared Automatic Interaction Detector)：基于卡方检验的方法，主要用于分类任务。它可以在多路分裂的情况下工作，并且能够自动检测变量之间的交互作用。\n",
    "* Random Forest 算法:随机森林是一种基于决策树的集成学习算法，它从原始训练数据中有放回地随机抽取多个子集，分别构建决策树，然后综合这些决策树的预测结果进行最终决策。在构建决策树时，对于每个节点的分裂，随机森林算法会随机选择一部分属性来计算分裂指标，而不是像传统决策树算法那样考虑所有属性。\n",
    "* GBDT 算法: 即梯度提升决策树算法，是一种基于梯度提升框架的集成学习算法。它通过迭代地训练决策树来拟合残差，每次训练的决策树都是对上一轮模型预测结果与真实值之间的残差进行学习，然后将所有决策树的预测结果相加得到最终的预测结果。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 决策树的特点\n",
    "* 易解释性：决策树形成的规则非常直观，容易被人类理解。\n",
    "* 非参数化模型：不需要假设数据服从特定的概率分布。\n",
    "* 可处理缺失值：某些实现可以处理含有缺失值的数据。\n",
    "* 快速训练与预测：一旦树建立起来，预测速度很快。\n",
    "* 对异常点不敏感：由于其基于多数投票的方式决定叶节点的类别，所以对外部异常点有一定的容忍度。\n",
    "然而，决策树也有一些缺点，比如容易产生过拟合，特别是在树长得太深时；另外，小的变化可能会导致完全不同的树结构。因此，在实际应用中常常会结合集成方法（如随机森林、梯度提升树等）以提高性能和稳定性。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 决策树概述\n",
    "决策树是属于有监督机器学习的一种，起源非常早，符合直觉并且非常直观，模仿人类做决策的过程，早期人工智能模型中有很多应用，现在更多的是使用基于决策树的一些集成学习的算法。这一章我们把决策树算法理解透彻了，非常有利于后面去学习集成学习。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|ID|name|aaa|\n",
    "|---|---|---|\n",
    "|1|张三|100|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 案例 一\n",
    "| ID|拥有房产（是/否） | 婚姻[单身，已婚，离婚] | 年收入（单位：千元） | 无法偿还债务（是/否） |\n",
    "|-------|-------|-------|-------|-------|\n",
    "| 1 | 是 | 单身 | 125 | 否 |\n",
    "| 2 | 否 | 已婚 | 100 | 否 | \n",
    "| 3 | 否 | 单身 | 70 | 否 | \n",
    "| 4 | 是 | 已婚 | 120 | 否 | \n",
    "| 5 | 否 | 离婚 | 95 | 是 | \n",
    "| 6 | 否 | 已婚 | 60 | 否 | \n",
    "| 7 | 是 | 离婚 | 220 | 否 | \n",
    "| 8 | 否 | 单身 | 85 | 是 | \n",
    "| 9 | 否 | 已婚 | 75 | 否 | \n",
    "| 10 | 否 | 单身 | 90 | 是 | \n",
    "\n",
    "\n",
    "上表根据历史数据，记录已有的用户是否可以偿还债务，以及相关的信息。通过该数据，构建的决策树如下：\n",
    "\n",
    "![image.png](https://i-blog.csdnimg.cn/img_convert/3d068917334ecd1fa40c27c1706fa64b.png)\n",
    "\n",
    "比如新来一个用户：无房产，单身，年收入55K，那么根据上面的决策树，可以预测他无法偿还债务（蓝色虚线路径）。从上面的决策树，还可以知道是否拥有房产可以很大的决定用户是否可以偿还债务，对借贷业务具有指导意义。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实例二\n",
    "女孩母亲要给她介绍对象，年龄是多少，母亲说24。长得帅吗？挺帅的。收入高吗？中等收入。是公务员吗？母亲说，是的。女孩：好，我去见见。\n",
    "<br>\n",
    "![image.png](https://i-blog.csdnimg.cn/img_convert/9956d91581cd63e04a7ef39424a946fb.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 决策树算法特点\n",
    "\n",
    "* 可以处理非线性的问题\n",
    "* 可解释性强，没有方程系数$\\theta$\n",
    "* 模型简单，模型预测效率高 if else\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DecisionTreeClassifier 使用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 算例介绍\n",
    "|日志密度|好友密度|是否使用真实头像|账号是否真实|\n",
    "|----|----|----|----|\n",
    "|s|s|no|no|\n",
    "|s|l|yes|yes|\n",
    "|l|m|yes|yes|\n",
    "|m|m|yes|yes|\n",
    "|l|m|yes|yes|\n",
    "|m|l|no|yes|\n",
    "|m|s|no|no|\n",
    "|l|m|no|yes|\n",
    "|m|s|no|yes|\n",
    "|s|s|yes|no|\n",
    "\n",
    "其中s、m和l分别表示小、中和大。\n",
    "\n",
    "账号是否真实跟属性：日志密度、好友密度、是否使用真实头像有关系~"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 构建决策树并可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['N' 'Y' 'Y' 'Y' 'Y' 'Y' 'N' 'Y' 'Y' 'N']\n"
     ]
    },
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日志密度</th>\n",
       "      <th>好友密度</th>\n",
       "      <th>真实头像</th>\n",
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       "      <td>l</td>\n",
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       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>Y</td>\n",
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       "      <td>l</td>\n",
       "      <td>m</td>\n",
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       "      <th>5</th>\n",
       "      <td>m</td>\n",
       "      <td>l</td>\n",
       "      <td>N</td>\n",
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       "      <th>6</th>\n",
       "      <td>m</td>\n",
       "      <td>s</td>\n",
       "      <td>Y</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>l</td>\n",
       "      <td>m</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>m</td>\n",
       "      <td>s</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>s</td>\n",
       "      <td>s</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  日志密度 好友密度 真实头像\n",
       "0    s    s    N\n",
       "1    s    l    Y\n",
       "2    l    m    Y\n",
       "3    m    m    Y\n",
       "4    l    m    Y\n",
       "5    m    l    N\n",
       "6    m    s    Y\n",
       "7    l    m    Y\n",
       "8    m    s    Y\n",
       "9    s    s    Y"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建数据\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "y = np.array(list('NYYYYYNYYN'))\n",
    "print(y)\n",
    "X = pd.DataFrame({'日志密度':list('sslmlmmlms'),\n",
    "                  '好友密度':list('slmmmlsmss'),\n",
    "                  '真实头像':list('NYYYYNYYYY'),\n",
    "                  })\n",
    "X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#模型训练（报错，原因：数据类型是字符串）\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>日志密度</th>\n",
       "      <th>好友密度</th>\n",
       "      <th>真实头像</th>\n",
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       "      <th>2</th>\n",
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   日志密度  好友密度  真实头像\n",
       "0     0     0     0\n",
       "1     0     2     1\n",
       "2     2     1     1\n",
       "3     1     1     1\n",
       "4     2     1     1\n",
       "5     1     2     0\n",
       "6     1     0     1\n",
       "7     2     1     1\n",
       "8     1     0     1\n",
       "9     0     0     1"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 修改数据(进行数据转化)\n",
    "X['日志密度'] = X['日志密度'].map({'s':0,'m':1,'l':2})\n",
    "X['好友密度'] = X['好友密度'].map({'s':0,'m':1,'l':2})\n",
    "X['真实头像'] = X['真实头像'].map({'N':0,'Y':1})\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/sklearn/tree/_export.py:673: UserWarning: Glyph 22909 (\\N{CJK UNIFIED IDEOGRAPH-597D}) missing from font(s) Hack Nerd Font Mono.\n",
      "  ann.update_bbox_position_size(renderer)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/sklearn/tree/_export.py:673: UserWarning: Glyph 21451 (\\N{CJK UNIFIED IDEOGRAPH-53CB}) missing from font(s) Hack Nerd Font Mono.\n",
      "  ann.update_bbox_position_size(renderer)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/sklearn/tree/_export.py:673: UserWarning: Glyph 23494 (\\N{CJK UNIFIED IDEOGRAPH-5BC6}) missing from font(s) Hack Nerd Font Mono.\n",
      "  ann.update_bbox_position_size(renderer)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/sklearn/tree/_export.py:673: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from font(s) Hack Nerd Font Mono.\n",
      "  ann.update_bbox_position_size(renderer)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/sklearn/tree/_export.py:673: UserWarning: Glyph 26085 (\\N{CJK UNIFIED IDEOGRAPH-65E5}) missing from font(s) Hack Nerd Font Mono.\n",
      "  ann.update_bbox_position_size(renderer)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/sklearn/tree/_export.py:673: UserWarning: Glyph 24535 (\\N{CJK UNIFIED IDEOGRAPH-5FD7}) missing from font(s) Hack Nerd Font Mono.\n",
      "  ann.update_bbox_position_size(renderer)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/events.py:82: UserWarning: Glyph 26085 (\\N{CJK UNIFIED IDEOGRAPH-65E5}) missing from font(s) Hack Nerd Font Mono.\n",
      "  func(*args, **kwargs)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/events.py:82: UserWarning: Glyph 24535 (\\N{CJK UNIFIED IDEOGRAPH-5FD7}) missing from font(s) Hack Nerd Font Mono.\n",
      "  func(*args, **kwargs)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/events.py:82: UserWarning: Glyph 23494 (\\N{CJK UNIFIED IDEOGRAPH-5BC6}) missing from font(s) Hack Nerd Font Mono.\n",
      "  func(*args, **kwargs)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/events.py:82: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from font(s) Hack Nerd Font Mono.\n",
      "  func(*args, **kwargs)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/events.py:82: UserWarning: Glyph 22909 (\\N{CJK UNIFIED IDEOGRAPH-597D}) missing from font(s) Hack Nerd Font Mono.\n",
      "  func(*args, **kwargs)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/events.py:82: UserWarning: Glyph 21451 (\\N{CJK UNIFIED IDEOGRAPH-53CB}) missing from font(s) Hack Nerd Font Mono.\n",
      "  func(*args, **kwargs)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 26085 (\\N{CJK UNIFIED IDEOGRAPH-65E5}) missing from font(s) Hack Nerd Font Mono.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 24535 (\\N{CJK UNIFIED IDEOGRAPH-5FD7}) missing from font(s) Hack Nerd Font Mono.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 23494 (\\N{CJK UNIFIED IDEOGRAPH-5BC6}) missing from font(s) Hack Nerd Font Mono.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from font(s) Hack Nerd Font Mono.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 22909 (\\N{CJK UNIFIED IDEOGRAPH-597D}) missing from font(s) Hack Nerd Font Mono.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 21451 (\\N{CJK UNIFIED IDEOGRAPH-53CB}) missing from font(s) Hack Nerd Font Mono.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 模型训练并可视化\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import tree\n",
    "\n",
    "model = DecisionTreeClassifier(max_depth=3, random_state=0)\n",
    "model.fit(X, y)\n",
    "plt.rcParams['font.family'] = 'Hack Nerd Font Mono'\n",
    "plt.figure(figsize=(12, 16))\n",
    "fn = X.columns\n",
    "_ = tree.plot_tree(model,filled=True,feature_names = fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 12.2.1 (20241206.2353)\n",
       " -->\n",
       "<!-- Title: Tree Pages: 1 -->\n",
       "<svg width=\"285pt\" height=\"325pt\"\n",
       " viewBox=\"0.00 0.00 284.50 324.50\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 320.5)\">\n",
       "<title>Tree</title>\n",
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      ],
      "text/plain": [
       "<graphviz.sources.Source at 0x12dab9e50>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据可视化的另外一种教程\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import graphviz\n",
    "from sklearn import tree\n",
    "model = DecisionTreeClassifier(criterion='entropy')\n",
    "model.fit(X,y)\n",
    "dot_data = tree.export_graphviz(model, out_file=None, \n",
    "                            feature_names= X.columns,# 特征名\n",
    "                            class_names=np.unique(y),# 类别名\n",
    "                            filled=True, # 填充颜色\n",
    "                            rounded=True) # 圆角\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph.render('Account',format='png')\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 信息熵\n",
    "* 构建好一颗树，数据变的有顺序了（构建前，一堆数据，杂乱无章；构建一颗，整整齐齐，顺序），用什么度量衡表示，数据是否有顺序：信息熵\n",
    "* 物理学，热力学第二定律（熵），描述的是封闭系统的混乱程度\n",
    "* 信息熵，和物理学中熵类似的\n",
    "  \n",
    "* $H(x) = -\\sum_{i=1}^{n} p(x) \\log_2 p(x)$\n",
    "\n",
    "* $H(x) = \\sum_{i=1}^{n} p(x) \\log_2 \\frac{1}{p(x)}$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 信息增益\n",
    "\n",
    "信息增益是知道了某个条件后，事件的不确定性下降的程度。写作 $g(X,Y)$。它的计算方式为熵减去条件熵，如下\n",
    "\n",
    "$ g(X,y) = H(Y)-H(Y|X)$\n",
    "\n",
    "表示的是，知道了某个条件后，原来事件不确定性降低的幅度。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 手动计算实现决策树分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日志密度</th>\n",
       "      <th>好友密度</th>\n",
       "      <th>真实头像</th>\n",
       "      <th>真实用户</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>Y</td>\n",
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       "      <td>Y</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
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       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
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      "text/plain": [
       "   日志密度  好友密度  真实头像 真实用户\n",
       "0     0     0     0    N\n",
       "1     0     2     1    Y\n",
       "2     2     1     1    Y\n",
       "3     1     1     1    Y\n",
       "4     2     1     1    Y\n",
       "5     1     2     0    Y\n",
       "6     1     0     1    N\n",
       "7     2     1     1    Y\n",
       "8     1     0     1    Y\n",
       "9     0     0     1    N"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据整合\n",
    "X['真实用户'] = y\n",
    "X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.8812908992306926)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#计算未划分信息熵\n",
    "\n",
    "s = X['真实用户']\n",
    "p = s.value_counts()/s.size\n",
    "(p * np.log2(1/p)).sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5 -0.689659695223976\n",
      "1.5 -0.6896596952239761\n"
     ]
    }
   ],
   "source": [
    "# 按照日志密度进行划分\n",
    "\n",
    "x = X['日志密度'].unique()\n",
    "x.sort()\n",
    "# 如何划分分成两部分\n",
    "for i in range(len(x)-1):\n",
    "    split = x[i:i+2].mean()\n",
    "    cond = X['日志密度']<=split\n",
    "    # 概率分布\n",
    "    p = cond.value_counts()/cond.size\n",
    "    # 按照条件划分，两边的概率分布情况\n",
    "    indexes = p.index\n",
    "    entropy = 0\n",
    "    for index in indexes:\n",
    "        user = X[cond==index]['真实用户']\n",
    "        p_user = user.value_counts()/user.size\n",
    "        entropy+=(p_user*np.log2(p_user)).sum()*p[index]\n",
    "    print(split,entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2]\n",
      "日志密度 0.5 0.689659695223976\n",
      "日志密度 1.5 0.689659695223976\n",
      "[0 1 2]\n",
      "好友密度 0.5 0.32451124978365314\n",
      "好友密度 1.5 0.763547202339972\n",
      "[0 1]\n",
      "真实头像 0.5 0.8490224995673064\n",
      "最佳列分条件是： {'好友密度': np.float64(0.5)}\n"
     ]
    }
   ],
   "source": [
    "# 筛选最佳划分条件\n",
    "columns = ['日志密度','好友密度','真实头像']\n",
    "lower_entropy = 1\n",
    "condition = {}\n",
    "for col in columns:\n",
    "    x = X[col].unique()\n",
    "    x.sort()\n",
    "    print(x)\n",
    "    # 如何划分呢，分成两部分\n",
    "    for i in range(len(x) - 1):\n",
    "        split = x[i:i+2].mean()\n",
    "        cond = X[col] <= split\n",
    "        # 概率分布\n",
    "        p = cond.value_counts()/cond.size\n",
    "        # 按照条件划分，两边的概率分布情况\n",
    "        indexs =p.index\n",
    "        entropy = 0\n",
    "        for index in indexs:\n",
    "            user = X[cond == index]['真实用户']\n",
    "            p_user = user.value_counts()/user.size\n",
    "            entropy += (p_user * np.log2(1/p_user)).sum() * p[index]\n",
    "        print(col,split,entropy)\n",
    "        if entropy < lower_entropy:\n",
    "            condition.clear()\n",
    "            lower_entropy = entropy\n",
    "            condition[col] = split\n",
    "print('最佳列分条件是：',condition)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n",
      "日志密度 0.5 0.5\n",
      "[0 1]\n",
      "真实头像 0.5 0.6887218755408671\n",
      "最佳列分条件是： {'日志密度': np.float64(0.5)}\n"
     ]
    }
   ],
   "source": [
    "# 进一步列分\n",
    "\n",
    "cond = X['好友密度'] < 0.5\n",
    "X_ = X[cond]\n",
    "columns = ['日志密度','真实头像']\n",
    "lower_entropy = 1\n",
    "condition = {}\n",
    "for col in columns:\n",
    "    x = X_[col].unique()\n",
    "    x.sort()\n",
    "    print(x)\n",
    "    # 如何划分呢，分成两部分\n",
    "    for i in range(len(x) - 1):\n",
    "        split = x[i:i+2].mean()\n",
    "        cond = X_[col] <= split\n",
    "        # 概率分布\n",
    "        p = cond.value_counts()/cond.size\n",
    "        # 按照条件划分，两边的概率分布情况\n",
    "        indexs =p.index\n",
    "        entropy = 0\n",
    "        for index in indexs:\n",
    "            user = X_[cond == index]['真实用户']\n",
    "            p_user = user.value_counts()/user.size\n",
    "            entropy += (p_user * np.log2(1/p_user)).sum() * p[index]\n",
    "        print(col,split,entropy)\n",
    "        if entropy < lower_entropy:\n",
    "            condition.clear()\n",
    "            lower_entropy = entropy\n",
    "            condition[col] = split\n",
    "print('最佳列分条件是：',condition)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 决策树分裂指标\n",
    "\n",
    "常用的分类条件是:\n",
    "* 信息增益\n",
    "* Gini 系数\n",
    "* 信息增益率\n",
    "* MSE(回归问题)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 信息熵(ID3)\n",
    "\n",
    "在信息论里熵叫作信息量，即熵是对不确定性的度量。从控制论的角度来看，应叫不确定性。信息论的创始人香农在其著作《通信的数学理论》中提出了建立在概率统计模型上的信息度量。他把信息定义为“用来消除不确定性的东西”。在信息世界，熵越高，则能传输越多的信息，熵越低，则意味着传输的信息越少。还是举例说明，假设 Dammi 在买衣服的时候有颜色，尺寸，款式以及设计年份四种要求，而 Sara 只有颜色和尺寸的要求，那么在购买衣服这个层面上 Dammi 由于选择更多因而不确定性因素更大，最终 Dammi所获取的信息更多，也就是熵更大。所以信息量=熵=不确定性，通俗易懂。在叙述决策树时我们用熵表示不纯度（Impurity）。\n",
    "\n",
    "对应公式如下:\n",
    " $$ H(x) = -\\sum_{i=1}^{n}p(x_i)log_2p(x_i) $$\n",
    "\n",
    "熵的变化越大，说明划分越纯，信息增益越大~\n",
    "\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gini系数(CART)\n",
    "基尼系数是指国际上通用的、用以衡量一个国家或地区居民收入差距的常用指标。\n",
    "\n",
    "基尼系数最大为“1”，最小等于“0”。基尼系数越接近 0 表明收入分配越是趋向平等。国际惯例把 0.2 以下视为收入绝对平均，0.2-0.3 视为收入比较平均；0.3-0.4 视为收入相对合理；0.4-0.5 视为收入差距较大，当基尼系数达到 0.5 以上时，则表示收入悬殊。\n",
    "基尼系数的实际数值只能介于 0～1 之间，基尼系数越小收入分配越平均，基尼系数越大收入分配越不平均。国际上通常把 0.4 作为贫富差距的警戒线，大于这一数值容易出现社会动荡。\n",
    "Gini 系数越小，代表集合中的数据越纯，所有我们可以计算分裂前的值在按照某个维度对数据集进行划分，然后可以去计算多个节点的 Gini 系数。\n",
    "\n",
    "对应公式如下:\n",
    "$$ gini = \\sum_{i=1}{n}p_i(1-p_i) $$\n",
    "\n",
    "在对数据进行分类是gini系数的变化越大，说明划分越纯，效果越好~\n",
    "\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 信息增益率\n",
    "大学期末的数学考试只有单选题。对于一个完全没有学习过的学生。该如何过关呢？\n",
    "4个选项是正确选项的概率都是1/4。那么单项选择题的答案的熵就是：\n",
    "$$ H(Y) = -0.25log2(0.25)*4 = 2bit $$\n",
    "在学霸圈做单项选择题有一个秘籍：三长一短选最短，三短一长选最长。姑且假设学霸的秘籍一般都是正确的。\n",
    "如果在某场考试中，有10%的单项选题是三长一短，10%的选题是三短一长。计算该考试单项选题的关于长短题的条件熵：\n",
    "|题目类型|答案概率|题目概率|\n",
    "|----|----|----|\n",
    "|三长一短|(1,0,0,0)熵是 0，结果确定！|10%|\n",
    "|三短一长|(0,1,0,0)熵是 0，结果确定！|10%|\n",
    "|一样长|(0.25,0.25,0.25,0.25)熵是 2，结果确定！|80%|\n",
    "\n",
    "\n",
    "计算条件熵（条件就是：题目不同类型）\n",
    "$H(Y|X)=0.1\\times 0+0.1 \\times 0 +0.8 \\times 2 = 1.6bit$\n",
    "那么信息增益是:\n",
    "$g(X,Y) = H(Y) -H(Y|X) = 2bit-1.6bit = 0.4bit$\n",
    "信息增益率在信息增益的基础上增加了惩罚项，惩罚项是特征的固有值。\n",
    "写作$gr(X,Y)$。定义信息增益除以特征的固有值，如下:\n",
    "$$ \\begin{aligned}\n",
    "gr(X,Y) = \\frac{g(X,Y)}{Info(X)} \\\\\n",
    "\n",
    "Info(X) = -\\sum_{v \\in values(X)} \\frac{num(v)}{num(X)} log_2 \\frac{num(v)} {num(X)}\n",
    "\n",
    "\n",
    "\\end{aligned} $$\n",
    "\n",
    "计算上面单选题题目长短案例的信息增益率：\n",
    "\n",
    "$$ \\begin{aligned}\n",
    "    Info(X) = -(0.1 \\times log_2(0.1) \\times 2 + 0.8 \\times log_2(0.8)) = 0.92 \\\\\n",
    "    gr(X,Y) = \\frac{g(X,y)}{Info(X)} = \\frac{0.4}{0.92} = 0.43\n",
    "\\end{aligned} $$\n",
    "\n",
    "\n",
    "对于取值多的属性，尤其一些连续型数值，这个单独的属性就可以划分所有的样本，使得所有分支下的样本集合都是“纯的”（最极端的情况是每个叶子节点只有一个样本）。\n",
    "一个属性的信息增益越大，表明属性对样本的熵减少的能力更强，这个属性使得数据由不确定性变成确定性的能力越强。\n",
    "所以如果是取值更多的属性，更容易使得数据更“纯”（尤其是连续型数值），其信息增益更大，决策树会首先挑选这个属性作为树的顶点。结果训练出来的形状是一棵庞大且深度很浅的树，这样的划分是极为不合理的。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MSE\n",
    "\n",
    "用于回归树，后面章节具体介绍\n",
    "\n",
    "![image](https://i-blog.csdnimg.cn/img_convert/6c890d03c94cff0edcc5a4944727cf96.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 鸢尾花分类代码实战\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 决策树分类鸢尾花数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " #加载数据\n",
    "\n",
    "import numpy as np # 导入numpy，用于数值计算\n",
    "import matplotlib.pyplot as plt # 导入matplotlib.pyplot，用于绘制图表\n",
    "from sklearn.datasets import load_iris # 导入sklearn.datasets中的load_iris函数，用于加载Iris数据集\n",
    "from sklearn.model_selection import train_test_split  # 导入train_test_split函数，用于数据集划分\n",
    "from sklearn.tree import DecisionTreeClassifier, export_text, plot_tree # 导入决策树分类器和相关函数\n",
    "from sklearn.metrics import accuracy_score # 导入accuracy_score函数\n",
    "from sklearn import datasets\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['Arial Unicode MS']\n",
    "\n",
    "iris = load_iris()\n",
    "X = iris.data\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "真实类别是： [0 2 1 0 2 1 0 1 1 1 2 2 2 0 0 1 2 1 0 2 1 0 1 1 2 0 0 1 0 0 2 0 2 2 1 2 0\n",
      " 0]\n",
      "算法预测是： [0 2 1 0 2 1 0 1 1 1 2 2 2 0 0 1 2 1 0 2 1 0 1 1 2 0 0 1 0 0 2 0 1 2 1 2 0\n",
      " 0]\n",
      "准确率是： 0.9736842105263158\n"
     ]
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   "source": [
    "\n",
    "X,y = datasets.load_iris(return_X_y=True)\n",
    "\n",
    "# 随机拆分\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,random_state = 256)\n",
    "\n",
    "# max_depth调整树深度：剪枝操作\n",
    "# max_depth默认，深度最大，延伸到将数据完全划分开为止。\n",
    "model = DecisionTreeClassifier(max_depth=None,criterion='entropy')\n",
    "model.fit(X_train,y_train)\n",
    "y_ = model.predict(X_test)\n",
    "print('真实类别是：',y_test)\n",
    "print('算法预测是：',y_)\n",
    "print('准确率是：',model.score(X_test,y_test))\n",
    "# 决策树提供了predict_proba这个方法，发现这个方法，返回值要么是0，要么是1\n",
    "model.predict_proba(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 决策树可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
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       "<graphviz.sources.Source at 0x12da8e520>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据可视化\n",
    "iris\n",
    "import graphviz\n",
    "from sklearn import tree\n",
    "# 导出数据\n",
    "dot_data = tree.export_graphviz(model,feature_names=iris.feature_names,# 特征名\n",
    "                     class_names=iris.target_names,# 类别名\n",
    "                     filled=True, # 填充颜色\n",
    "                     rounded=True,)\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 决策树剪枝"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "真实类别是： [0 2 1 0 2 1 0 1 1 1 2 2 2 0 0 1 2 1 0 2 1 0 1 1 2 0 0 1 0 0 2 0 2 2 1 2 0\n",
      " 0]\n",
      "算法预测是： [0 2 1 0 2 1 0 1 1 1 2 2 2 0 0 1 2 1 0 2 1 0 1 1 2 0 0 1 0 0 2 0 1 2 1 2 0\n",
      " 0]\n",
      "准确率是： 0.9736842105263158\n"
     ]
    },
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      ],
      "text/plain": [
       "<graphviz.sources.Source at 0x12d6e30b0>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 决策树减枝算法\n",
    "\n",
    "# 设置图片的尺寸\n",
    "# 鸢尾花4个属性\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "X = iris['data']\n",
    "y = iris['target']\n",
    "fn = iris['feature_names']\n",
    "# 随机拆分\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,random_state = 256)\n",
    "\n",
    "# max_depth调整树深度：剪枝操作\n",
    "# max_depth默认，深度最大，延伸到将数据完全划分开为止。\n",
    "# min_impurity_decrease（节点划分最小不纯度）如果某节点的不纯度(基尼系数，信息增益，均方差)小于这个阈值，则该节点不再生成子节点\n",
    "# max_depth（决策树最大深度）；min_samples_split（内部节点再划分所需最小样本数）\n",
    "# min_samples_leaf（叶子节点最少样本数）；max_leaf_nodes（最大叶子节点数）\n",
    "model = DecisionTreeClassifier(criterion='entropy',min_impurity_decrease=0.2)\n",
    "model.fit(X_train,y_train)\n",
    "y_ = model.predict(X_test)\n",
    "print('真实类别是：',y_test)\n",
    "print('算法预测是：',y_)\n",
    "print('准确率是：',model.score(X_test,y_test))\n",
    "# 导出数据\n",
    "dot_data = tree.export_graphviz(model,feature_names=fn,\n",
    "                     class_names=iris['target_names'],# 类别名\n",
    "                     filled=True, # 填充颜色\n",
    "                     rounded=True,)\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选择合适的超参数并可视化\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "错误率为31.579%\n",
      "错误率为2.632%\n",
      "错误率为2.632%\n",
      "错误率为2.632%\n",
      "错误率为2.632%\n",
      "错误率为2.632%\n",
      "错误率为2.632%\n",
      "错误率为2.632%\n",
      "错误率为2.632%\n",
      "错误率为2.632%\n",
      "错误率为2.632%\n",
      "错误率为2.632%\n",
      "错误率为2.632%\n",
      "错误率为2.632%\n",
      "错误率为2.632%\n"
     ]
    },
    {
     "data": {
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523/mgIKIUAT4gJa10JegKgSxeKEokiYY1Bd0JC2ZEW3RUO+mJUMieaFIX/qxbgoLkaFI2//+978HlwUJPTYFiljH4AUiUajRpI1aKkRf+CeddJILHbHoMxUWFLq0nploMsn9+/dnem20UORRNSiy0uOFIi15otmqIymk1ahRw+3z3HPPDXtOk2l6C/VGExmKIukcFChVJdNkl8cff3ygffv2LhyHLomi4/7ggw8Cd999t1t+RRNLZvW5QDJI0X/i3YQHwB/UV+n33393/Y7iSXP4aMJDzYd0OJqfSX2EIieqLMjU50udvfX7CJ1/KSvqS6b+XrnRSR9IVIQiAAAAhuQDAABkIBQBAAAQigAAADIQigAAAJjROvu0fMHatWvdtPvJNAoFAIBkFggEbPv27W7E6eFmzCcUZZMCkabKBwAABc+qVavcgs1ZIRRlk7cwoy6qVtNOJpp/RCuHt27d2q1Q7kd+vwacv7/PX/x+Dfx+/sl8DdLT011RIzuLTROKsslrMlMgSsZQVKJECXdeyfQXISf8fg04f3+fv/j9Gvj9/P1wDVKy0fWFjtYAAACEIgAAgAyEIgAAAEIRAABABkIRAAAAoQgAACADoQgAAIBQBAAAkIFQBAAAwIzWCeDgQbNZs8zWrTM7/niz5s3NUlPjfVQAAPgOoSie3nnH7N57zVav/v9tWqzuuefM2rWL55EBAOA7NJ/FMxC1bx8eiGTNmozteh4AAOQbQlG8msxUIQoEMj/nbbvvvozXAQCAfEEoigf1IYqsEEUGo1WrMl4HAADyBaEoHtSpOjdfBwAAjhqhKB40yiw3XwcAAI4aoSgeNOxeo8xSUqI/r+3Vq2e8DgAA5AtCUTxoHiINu5fIYOQ9fvZZ5isCACAfEYriRfMQvf22WdWq4dtVQdJ25ikCACBfEYriScHnl1/MOnTIeHzttWYrVhCIAACIA0JRvKmJ7JxzMu6npdFkBgBAnBCKEkHlyhk/GYIPAEDcEIoSgTf0/rff4n0kAAD4FqEoEVApAgAg7ghFiVQp2rrVbM+eeB8NAAC+RChKBGXLmhUtmnGfJjQAAOKCUJQINGGj14RGKAIAIC4SPhTt3r3bunXrZqVKlbJKlSrZwIEDY752/Pjxdsopp7jXXnzxxbZs2bJMz9esWdNKlChhl19+ua1fv94SBv2KAACIq4QPRb1797b58+fbV199ZRMmTLDBgwfbxIkTM73us88+s1tuucUeffRRW7BggZ122mnWtm1b279/v3t+zpw5duutt9qwYcPc/vbt22ddunSxhMEINAAA4qpQoleJRo0aZUOGDLF69epZy5YtrUePHjZixIhMr123bp3169fPrrvuOqtRo4b179/fFi9ebIsWLXLPv/jii9axY0dr166dnXTSSfbyyy/btGnTbPny5ZYQqBQBABBXCR2K5s2b5yo9TZs2DW5r1qyZzZ071wKBQNhrr732WheERO95VguqmlmZMmXczy+//NKah6w6r+BUtWpVV0FKCFSKAACIq8KWwFavXm3ly5e3IkWKBLdVqVLF9uzZY5s3b3bPRZo+fbq1adPGBSOFJPUh8valEBRK+1qzZk3Uz967d6+7edLT091P7ddrkstNKRUrul/GoTVr7GAe7D8r3vnkxXkVFH6/Bpy/v89f/H4N/H7+yXwN9ufgfBI6FCn8FPWGqv/Be6ymtWgaNGhg33zzjX344YcuIG3fvt11vI61r1j7GTRokA0YMCDT9qlTp7qO2rmt0qpV1kTha/Fi+3TKFIsHNSf6nd+vAefv7/MXv18Dv59/Ml6DXbt2JUcoKlasmAszobzqTfHixaO+p0KFCu6mcDRr1izXH0nhJta+Yu2nT58+1rNnz7BKUfXq1a1169ZWunRpy20plSqZDRxoZfbssUsvvdTyO0XrL0GrVq0sTYvS+pDfrwHn7+/zF79fA7+ffzJfg/Q/WnoKfChSc5eayfSL8n5Bau5SwClXrlzYa9XPKDU11c4888zgtrp169rXX38d3Jc6Y4fSviKb1EKrSJGVJdFx5MkflmrV3I+U9estLTXVrFD+d/fKs3MrQPx+DTh/f5+/+P0a+P38k/Ea5ORcErqjdaNGjaxw4cI2e/bs4DZVf8455xxL0YSHIUaPHh3saO1RIDrhhBPc/SZNmrj3elasWOFCkbYnBFWK5MABs82b4300AAD4TkKHIvXd6dq1q/Xq1csWLlzo+ggNHz7cunfv7p7ftGlTsDnt9ttvd2W/f/7zny7wqMlMYequu+4KPq/5jSZPnuyG4Wsfl1xyidWqVcsSgpKs13GcYfkAAOS7hA5FMnToUKtfv741btzYOnXqZH379rUOHTq459R3yJvIUVWlf/3rX24+Ik3c+NFHH7mQpPveUH49p3mOTj31VFdOGzt2rCUUhuUDABA3Cd2nSEqWLGnjxo1zt0iRcxWpg3JWnZS1XIhuCUsTOP7wA5UiAADiIOErRb5CpQgAgLghFCUSlvoAACBuCEWJhEoRAABxQyhKJFSKAACIG0JRIqFSBABA3BCKEjEUUSkCACDfEYoSsfls+3aznTvjfTQAAPgKoSiRlCqlabwz7tOEBgBAviIUJRKt50ZnawAA4oJQlGjobA0AQFwQihINlSIAAOKCUJRoqBQBABAXhKJEQ6UIAIC4IBQlGipFAADEBaEo0VApAgAgLghFiYZKEQAAcUEoStRK0YYNZgcPxvtoAADwDUJRoqlY0axQIbNDh8w2boz30QAA4BuEokSTmpoRjIR+RQAA5BtCUSI3odGvCACAfEMoSuTO1lSKAADIN4SiRMSwfAAA8h2hKBExLB8AgHxHKEpEVIoAAMh3hKJERKUIAIB8RyhKRFSKAADId4SiRK8UBQLxPhoAAHyBUJTIlaJdu8y2b4/30QAA4AuEokR0zDFmpUpl3KdfEQAA+YJQlKjoVwQAQL4iFCUqRqABAJCvCEWJikoRAAD5ilCUqKgUAQCQrwhFiYpFYQEAyFeEokRF8xkAAPmKUJSoaD4DACBfEYoSFZUiAADyFaEo0StFmzaZ7d8f76MBACDpEYoSVblyZoULZ9xfvz7eRwMAQNIjFCWqQoXMKlXKuE+/IgAA8hyhKJHRrwgAgHxDKEpkjEADACDfEIoSGZUiAADyDaEokVEpAgAg3xCKEhmVIgAA8g2hKJFRKQIAIN8kfCjavXu3devWzUqVKmWVKlWygQMHxnztpEmTrEGDBlayZElr3LixffHFF8Hntm/fbikpKWG38uXLW0JjUVgAAPLNH7MDJq7evXvb/Pnz7auvvrI1a9bYtddea7Vq1bJOnTqFvW7WrFnWpUsXe+WVV6xZs2Y2evRoa9OmjS1evNgqV65sixYtsrJly9rSpUuD7ymkuYAKSvNZIGCWkhLvIwIAIGkVSvQq0ahRo2zIkCFWr149a9mypfXo0cNGjBiR6bVjxoyxDh062PXXX281a9a0Rx991IWh999/3z3/448/2sknn+yqQ97tuOOOswIRivbtM9u6Nd5HAwBAUkvoStG8efNs//791rRp0+A2VYEGDRpkgUDANYF57r77bktLS8u0j507d7qfqhQpFBUoxYqZlS2bEYhULTr22HgfEQAASSuhQ9Hq1atdRadIkSLBbVWqVLE9e/bY5s2bw/oENWrUKOy9H3/8sS1ZssRatGgRrBQpYF1wwQX2yy+/2Pnnn+8qUOqnFM3evXvdzZOenu5+ah+65ZfClStbytatdmD1agvkUajzzic/zyvR+P0acP7+Pn/x+zXw+/kn8zXYn4PzSehQpPBTtGjRsG3eYzWtxaJ+RJ07d7aOHTtaw4YN3baffvrJypUrZ0899ZTbR58+feyyyy6zOXPmWGpqaqZ9qBo1YMCATNunTp1qJUqUsPzSNC3NKpjZ9x99ZKuzOOfcMG3aNPM7v18Dzt/f5y9+vwZ+P/9kvAa7du1KjlBUrFgxF4xCedWb4sWLR33PihUrXN8jVZRGjhwZ3L5gwYKwUPXWW2+5PkcKRaHNcx6Fpp49e4ZViqpXr26tW7e20qVLW35JfeMNsx9+sEaVK1vDSy/NsxStvwStWrWK2gTpB36/Bpy/v89f/H4N/H7+yXwN0v9o6Snwoahq1aqumUy/KO8XpBFoCkuq+kTSyDI1l1WoUMH9YjWM3xNZcdJrtI+1a9dG/Wy9PvI9ouPI1z8sVaq4H6kbNlhqHn9uvp9bAvL7NeD8/X3+4vdr4PfzT8ZrkJNzSejRZ+onVLhwYZs9e3bY0PtzzjknrJO1bNq0yaVbVYhmzJgR1t9IpbNjjz02rCSo/kp6j0a1JTQmcAQAIF8kdChS352uXbtar169bOHChTZ9+nQbPny4de/e3T2vUOM1p/Xv39927Nhh48ePd5UlPaebApH2owqS5jz69ttv3bxHGrrftm1bO/XUUy2hsdQHAAD5IqFDkQwdOtTq16/vZqjWhI19+/Z18xF5TWATJ050999++23X1FanTh233bs9/fTT7nlN5njWWWfZxRdfbOedd56dcMIJNm7cOEt4VIoAAMgXCd2nSLRkh8JLtACjuYo8GzduzHI/ms1as13rVqBQKQIAIF8kfKXI97xK0e+/a+hdvI8GAICkRShKdJrF2pu8kiY0AADyDKEo0WmUHU1oAADkOUJRQUBnawAA8hyhqCCgUgQAQJ4jFBUEVIoAAMhzhKKCgEoRAAB5jlBUEFApAgAgzxGKCgIqRQAA5DlCUUFApQgAgDxHKCpIlSKFokOH4n00AAAkJUJRQVCpUsbPAwfMtmyJ99EAAJCUCEUFgZb5KFcu4z79igAAyBOEooKCfkUAAOQpQlFBC0VUigAAyBOEooKCYfkAAOQpQlFBQfMZAAB5ilBUUFApAgAgTxGKCgoqRQAA5ClCUUFBpQgAgDxFKCooqBQBAJCnCEUFrVKUnm62a1e8jwYAgKRDKCooSpc2K1484z7VIgAAch2hqKBISaFfEQAAeYhQVJDQrwgAgDxDKCpIqBQBAJBnCEUFCZUiAADyDKGoIKFSBABAniEUFcRKEaEIAIBcRygqSGg+AwAgzxCKChKazwAAyDOEooJYKdqwwezgwXgfDQAASYVQVJBUqJAxieOhQ2YbN8b7aAAASCqEooKkcGGzihUz7tOvCACAXEUoKmjoVwQAQJ4gFBU0jEADACBPEIoKGipFAADkCUJRQUOlCACAPEEoKmioFAEAkCcIRQUNlSIAAPIEoaigoVIEAECeIBQV5EVhA4F4Hw0AAEmDUFRQK0W7dpnt2BHvowEAIGkQigqakiUzbkITGgAAuYZQVBDR2RoAgFxHKCqI6GwNAID/QtHu3butW7duVqpUKatUqZINHDgw5msnTZpkDRo0sJIlS1rjxo3tiy++CHt+yJAhdvzxx7vnu3TpYjsKap8cKkUAAPgvFPXu3dvmz59vX331lU2YMMEGDx5sEydOzPS6WbNmuaDTp08fW7BggV188cXWpk0b++2P4PD222+7QKX3fvnll+41vXr1sgKJShEAAP4KRaoSjRo1ylV46tWrZy1btrQePXrYiBEjMr12zJgx1qFDB7v++uutZs2a9uijj1rlypXt/fffd88///zzds8999iFF17oqknPPvusjR071nZpFFdBQ6UIAAB/haJ58+bZ/v37rWnTpsFtzZo1s7lz51ogYo6eu+++2x544IFM+9i5c6d7rSpNzZs3D25v0qSJHThwwL7//nsrcKgUAQCQ6wpbAlu9erWVL1/eihQpEtxWpUoV27Nnj23evNk952nUqFHYez/++GNbsmSJtWjRwjZt2uTeU7Vq1eDz2me5cuVszZo1UT9779697uZJT093PxXSdIunlAoV3C8usG6dHciFY/HOJ97nFU9+vwacv7/PX/x+Dfx+/sl8Dfbn4HwSOhQpyBQtWjRsm/dYTWuxLF682Dp37mwdO3a0hg0b2qpVq8LeG7qvWPsZNGiQDRgwINP2qVOnWokSJSyeSq9YYS3MbN/KlfbRlCm5tt9p06aZ3/n9GnD+/j5/8fs18Pv5J+M1yEk3mTwLRb/++qurtJxyyilHvI9ixYq5YBTKq94UL1486ntWrFjh+h6pojRy5MjgfiTavmLtRx22e/bsGVYpql69urVu3dpKly5tcbVhg1mPHlYkPd0ubdXKLC3tqFO0/hK0atXK0o5yXwWV368B5+/v8xe/XwO/n38yX4P0P1p6ci0UaYfqf3PcccfZ8uXLrXbt2ples2HDBmvbtq3r19O+fXs38mvp0qXufUdKzV1qJtMvyvsFqblLIUdNX5H0eWouq1ChgvvFahi/6LWqCq1bt8512JZ9+/a5ZrXQJrVQen1kZUl0HHH/w6KO1qmplnLwoKX9/rsuVK7sNiHOLc78fg04f3+fv/j9Gvj9/JPxGuTkXLLV0Vpz/lSsWNGefPJJq1Onjr3xxhuZXnPdddfZ119/baNHj7ZDhw65bZGdoXNK/YQKFy5ss2fPDht6f84551hKSkrYaxVwlG5VIZoxY0ZYf6NChQq59+i9ns8//9z1K4rsi1QgFCpkVqlSxn06WwMAkL+jzxRwFCAUeDSZ4nfffRc2HF5BRFWYd955x4WQ3KC+O127dnXzCS1cuNCmT59uw4cPt+7duweDkNec1r9/fzcZ4/jx411lSc/p5rUl3nnnnW4Y/ieffGI//vijm//opptuitl8lvAYlg8AQK7KUXpRk5gmQFTfHM0H5PXo1jZVbl599dVg/53cMnToUKtfv76rVnXq1Mn69u3r5iMSNZN5EzlqckY1tamSpe3e7emnn3bP670KQvp59tln22mnnebmPyqwGJYPAECuynFHa/UZ0qSHGuH1wgsvuGapZcuW2eWXX+7u5zYtyTFu3Dh3ixTaPLdx48bD7kuBSrekQKUIAIBcleN2LlWEHn74YTdbtCo4auI6+eST7dZbb3XD29VvR81WyGNUigAAiG+lSJ2p1ZS1aNEiK1OmjNumfj7/+Mc/XLOW+vi88soruXuUyIxKEQAA8a0Uae2xM88803VYlueee84uvfRSt8aY+hqdeuqpbjV75DEqRQAAxDcUadi7RnlpLqL777/fdVpWB2xv3TE9VkhCHqNSBABA/jWfffTRR7Zy5UrbunWre6yh7A899JALQVpC45lnnnFD8ydNmuQWadVIr/Xr17uV7bds2eLeo9dEm68odLZoHGWlSNc3Yt4mAACQi6FITWP//e9/XedqBZuzzjrLhg0bZrfffrvbXrduXTc/kVacf+SRR9x7FIoef/xxNxpM71M1KZT2o+2EolwKRZqnads2s7Jl431EAAAkbyi64YYbXKdqdaJW5UeTMt511132xRdf2AknnOBeU7NmTfv555/tlltuCYYiDXvXCDWNQtMcRsgDmnRSHd0ViFQtIhQBAJB3oUgTNMqbb77pQtGCBQvstttuc3MGqdqjm2aQ1nNalV4drbUG2h133OGqTApFf/vb347uCJF1vyKFIvUr+mNNNwAAkA8drWvUqOGazdR8pmYwjUI799xz7bLLLnPbRbNce32QkMcYgQYAQK45okXKmjZtaqVLl3brkokqRQ0bNnT3NSTfW48M+TQCjVAEAED+T97o9TVaunSpXX311faf//zH9Se6+eabbdWqVVa1atWjPypkD8PyAQCIb6VIFaInnnjCBSEt8aG+RBqOryH7yEc0nwEAEL9QpNFk6lxdqlQp69+/v9WrV88tDKs+Rlr/TAEJ+YRKEQAA8QlFa9assUcffdTS0tJs2rRp1qxZM7e9S5cu1qJFCzfRo+YxQj6hUgQAQP6GIm9G6hUrVri5ivr06eOW8wil5jS9TkPxNQIN+YBKEQAA+dvResyYMbZz504777zzbPbs2dagQYNMr9Gs1prxWhUjiba0B/KoUqQlVTTir2jReB8RAADJHYoUeDyNGjWK+bqPP/7Y9TWS1157zXbt2pUbx4hYjjvOLC1Nk0NpKnGzP2YZBwAA+TQkPxYvEElk8xrygBaBVbVo1aqMfkWEIgAA8ndIPhII/YoAAEjMUPTvf//brY0W6zn1T0IuYgQaAACJGYr+/ve/20033ZRp+8GDB613795uwsfPP/88tz/Wv6gUAQCQGKFII87KlClz2Nf17dvXli1bZvXr1w/Ob4RcQKUIAID86Wh91VVX2fLlyzNtP/HEE+3dd991Q/V37NiR5T40Em3w4MGWmppqI0aMOLojRjgqRQAA5E8oWrx4sbtFOnDgQLY+4JVXXrG77rrLUlJSXDDSXEfIg1BEpQgAgLwfkq9As2HDhuCkjBUrVjzse5YuXWoPPvigvf322+79Q4cOtfvuu+/ojhaZ0XwGAED+zlNUrly5HO24bt26LkCddNJJ9vLLL9uFF154JMeH7FaKNHnjoUNmhZhlAQCAuE7eqOU/tFDsnj173ONLLrnEOnbsaNddd53bjjxSqVLGT81qreU+ypeP9xEBAODvUNS8efOwxxUqVLDatWsTiPJakSIq45lt3pzR2ZpQBADAEcm1tpYrrrjC2rZtayVKlHCPx44da+eff761b9/etqiCgbxDvyIAABInFGl4vmasrlatmns8cuRIq1Klir3zzjvWtGlTW716dW59FCIxLB8AgPwLRdOnT7dPPvnE3bJDM1cvWbLErrzySvdTfYy2b99+NMeKWKgUAQCQP6FIo8hatmxprVq1crfsKl68uBuSf80119jChQvtr3/969EcK2KhUgQAQN53tFZfobVr12baXrVq1Wx9gGaxHjVqlH322Wc2fvx4u/HGG61FixZHdrSIjkoRAAB5H4qeeuqpLJ9v3bq1NWjQIOpzL774oj377LN2ww032DPPPGOdO3e2v/3tb/bVV18d+REjMypFAADEv6P1c889Z5MmTQo+LlWqlJUuXdrdv/766y09Pd0GDRpkZ5xxhpsJ+5tvvnFVI+QiKkUAAORvKFK/oMNRFej3339398uUKePWO9u7d6+rEP3lL3+xfv36WbNmzY78iJEZlSIAAPJv8sYhQ4ZYnz597JFHHnHBRpYtW2aHtLREFho3bmz169e39957zz766KMcddRGDitF27aZ7d6tHu7xPiIAAJIzFPXv398GDhxohQoVst360v3DaaedZvv27bPsjmB76KGHCEV5oUwZs2LFzLTEiprQateO9xEBAJB8oejVV1+1J554wkqWLGmTJ0/OFGo0Cu3EE0/Mch8KTuvXr3edrpEHUlIymtBWrMhoQiMUAQCQN6GoSJEi9sEHH2Ra30y04OvTTz+d809G7jehKRTR2RoAgLwJRW+++abNnDkzaiBCAqGzNQAAeRuKjj/+eOvYsWPU59TxWh2pkQAYlg8AQP6MPotGHaeRIKgUAQAQv8kbX3rpJXv88ceP7giQO6gUAQCQP6Hohx9+sNdff902b94c3KbRZA8//PDRHQFyB5UiAADyp/nsrbfecnMVzZo1y5o2bZrp+U2bNrkO2dnVrl277B8lDo9KEQAAeReKtm7dart27XL3t2/fHgw/a9eudfcPHDgQtgRI+/btLUVz5mTDwYMHs/U6TRbZvXt3t75aiRIl7N5777W+fftm+R6Fs65du9oKDVEPUb58+bBKl3demoMpaSpFGzbo4pqlpsb7iAAASJ5QdOedd7oKUairr746bJbqyBCkKlKjRo1swoQJtm3bNrvrrrvc9sjH2dW7d2+bP3++W1NtzZo1du2111qtWrWsU6dOUV+vcKbRckWLFg3brskjFYiWL1/uFq31JEUgkooVMyZxVCDatMmsUqV4HxEAAMkTimrXrm1nnnmmu6/q0G+//WannHJKMEgogGix18jQ1LNnT/vf//7nQtDzzz/vtkc+zm6VaNSoUfbhhx9avXr13K1Hjx42YsSIqKHo5Zdftl69etlJJ50UXJTW8+OPP1qFChVcoEpKhQubVaiQUSlSvyJCEQAAudfRWst7zJ071926devmtimkeNtq1KhheWnevHm2f//+sD5MzZo1c5+tKlUkLTg7ZswY18QWadGiRXbyySdbUqNfEQAA8ZmnKK+tXr3a9QPSMiOeKlWq2J49e1xTmJ4L9c4777ifCkaRVCk6dOiQXXrppW4knSpgw4YNi7lumypgoVWw9PR091MhTbdElFq5shWaP98OrF5tgRwco3c+iXpe+cHv14Dz9/f5i9+vgd/PP5mvwf4cnE9ChyKFn8i+Qd5jNa3lxE8//eQ6iWsKgYoVK9qTTz5pLVq0cE2AoX2MPIMGDbIBAwZk2j516lTX4TsRnXHggJ1gZks+/dSWqikth6ZNm2Z+5/drwPn7+/zF79fA7+efjNfAGzB21KHogQcecE1SskF9VcysS5cuwVAQObortxUrVswFo1Be9aZ48eI52teUKVPcaDnvfa+99ppVr17d3nvvPevcuXPUJUzUNyq0UqTXt27d2kqXLm2JqNDnn5t98onVKVPGTr700hylaP0laNWqlaWlpZkf+f0acP7+Pn/x+zXw+/kn8zVI/6Ol56hD0cqVK23BggVh2zR6K1Tk6DOFD1VkNm7c6B7369fP9f/xHnvD6TXn0eFUrVrVNZPpF+X9gjQCTWGpXLlylhN6f+gvWRWnmjVrBqcXiKTnI6tU0faTUKpVcz9SN2yw1CM4xoQ+t3zi92vA+fv7/MXv18Dv55+M1yAn55JlR+tXX33VjeLS7f7773cBSJUjPd6yZUvUjsuffPKJa5rS8wpDaoYKfaz7Tz31VLYOTkP7CxcubLNnzw5u0+SR55xzTrbnQ/Jo1NzIkSODj3fu3GlLlixxI9qSBh2tAQA4YllWitTU5DU3qWqiUKPh+GXKlHHbChX6/0xVt25dF6Jyk5rpNAmjhtmPHTvWNeENHz7crbkmqkipP1C0ik6ktm3b2mOPPWZ16tSxSpUq2YMPPuiG57dp08aSBkt9AABwxLLd0fqaa65xzVah/WmaNGniAobop5q2NCLstttuC75GQ/g1a/Qdd9zh3p9TQ4cOdRM+Nm7c2AUyNb916NDBPad5hxTEbrzxxsPuRxWr1NRUu+6669xM3RdddJHrT6RtSYNKEQAAeR+K1JSlYexaFFYjtmTGjBmuQuRR9UXNUaGhSEFJo7jUKfu5557L8QEqCI0bN87dIkWbq0gUkiKDkqpJgwcPdrek5VWKdu4027FDFy/eRwQAQIGRZZ+iaEKDyK+//upCT6znRTNQq7ntH//4h02fPv1ojhWHoxB0zDEZ96kWAQCQt6EokobMf/fdd/btt99meqzb0qVL7corr3QTJ9500005GhqHI0C/IgAAcr/57Ouvv3ar06tDcqwlPX7++Wc766yzwqpHoY8jh/ircqR+RsjDfkXLllEpAgAgN0OR5hxSH5zQ4e/qG6TOyuo4LRr91bx5c3f/gw8+sGOOOcYuuOCCTPtatWqVnXbaada/f/+cHiNygkoRAAC5H4q6d+9uLVu2tF9++cWWLVvm+gR98cUX9tZbb7kKksKSJljUKC5viH61atWCjxEHjEADACD3+xRp1mitUK9lMB566CEXijQJo0aCadSZOlVrTbF27drleC0y5HGliFAEAEDedrTWhIo33HCDG5Y/evRoN7mjmtnmz5+f010hL9B8BgBA3s5TFI3mAtLcRQpImlxRlaREXUHeN2g+AwAg/0ORNGzY0N0kWgdr5DMqRQAAxGeeIiRopWjjRrMDB+J9NAAAFBiEomRTvryZ1nPTzOIbNsT7aAAAKDAIRclGgahixYz79CsCACDbCEXJiH5FAADkGKEoGTECDQCAHCMUJSMqRQAA5BihKBlRKQIAIMcIRcmIShEAADlGKEpGVIoAAMgxQlEyYlFYAAByjFCU7M1nmsQRAAAcFqEomZvP9uwx27Yt3kcDAECBQChKRsWLm5Upk3GfztYAAGQLoShZ0dkaAIAcIRQlK4blAwCQI4SiZEWlCACAHCEUJSsqRQAA5AihKFlRKQIAIEcIRcmKShEAADlCKEpWVIoAAMgRQlGyolIEAECOEIqSvVK0ebPZvn3xPhoAABIeoShZlStnlpaWcZ9qEQAAh0UoSlYpKf9fLSIUAQBwWISiZEZnawAAso1QlMzobA0AQLYRipIZlSIAALKNUJTMqBQBAJBthKJkRqUIAIBsIxQlMypFAABkG6EomVEpAgAg2whFfqkUBQLxPhoAABIaoSiZVaqU8XP/frMtW+J9NAAAJDRCUTIrWtTsuOMy7tOvCACALBGKkh39igAAyBZCkV/6FRGKAADIEqEo2bEoLAAAyRGKdu/ebd26dbNSpUpZpUqVbODAgYd9z8yZM61WrVqZto8fP95q1qxpJUqUsMsvv9zWr19vSY9KEQAAyRGKevfubfPnz7evvvrKJkyYYIMHD7aJEyfGfP3ChQutY8eOFogYgj5nzhy79dZbbdiwYW5/+/btsy5duljSYwJHAAAKfihSlWjUqFE2ZMgQq1evnrVs2dJ69OhhI0aMiPr6l19+2Zo0aWKVvSajEC+++KILS+3atbOTTjrJvXbatGm2fPlyS2p0tAYAoOCHonnz5tn+/futadOmwW3NmjWzuXPnZqoEyUcffWRjxoyxe++9N9NzX375pTVv3jz4uEaNGla1alVXQUpqVIoAAMiWwpbAVq9ebeXLl7ciRYoEt1WpUsX27Nljmzdvds+Feuedd9xPBaNo+1IICqV9rVmzJupn792719086enp7qdCmm4FRrlylmZmgXXr7ECM4/bOp0CdVy7z+zXg/P19/uL3a+D380/ma7A/B+eT0KFI4aeoJiAM4T1W01pu7CvWfgYNGmQDBgzItH3q1Kmuo3ZBUXjHDrvMzFK2bbOP3n3XDkVcg1BqTvQ7v18Dzt/f5y9+vwZ+P/9kvAa7du1KjlBUrFgxF2ZCedWb4sWL58q+Yu2nT58+1rNnz7BKUfXq1a1169ZWunRpKzACAQvcfLOl7N1rbRo1MosyKk8pWn8JWrVqZWlpqiv5j9+vAefv7/MXv18Dv59/Ml+D9D9aegp8KFJzl5rJ9IvyfkFq7lLAKVeuXI73tS6is7H2FdmkFlpFiqwsiY6jwP1hUb+iX36xtM2bzU45JebLCuS55TK/XwPO39/nL36/Bn4//2S8Bjk5l4TuaN2oUSMrXLiwzZ49O7ht1qxZds4551hKSkqO9qVRaXqvZ8WKFS4UaXvSYwQaAAAFOxSp707Xrl2tV69ebv6h6dOn2/Dhw6179+7u+U2bNoV1hs7K7bff7uY3mjx5shuGr31ccsklUSd5TDqMQAMAoGCHIhk6dKjVr1/fGjdubJ06dbK+fftahw4d3HMVKlTIciLHUBrKr7mKNM/Rqaee6sppY8eONV+gUgQAQMHuUyQlS5a0cePGuVukaHMVyY033uhukbRciG6+w1IfAAAU/EoRcgGLwgIAcFiEIj+gUgQAwGERivyAjtYAABwWochPzWfr15sdPBjvowEAICERivygYkUzzeukQKQJHAEAQCaEIj/QbJ7e4rn0KwIAICpCkV/QrwgAgCwRivyCCRwBAMgSocgvqBQBAJAlQpFfUCkCACBLhCK/oFIEAECWCEV+QaUIAIAsEYr8gkoRAABZIhT5BZUiAACyRCjyW6Vox46MGwAACEMo8otSpcyOOSbjPk1oAABkQijyE5rQAACIiVDkJ3S2BgAgJkKRn1ApAgAgJkKRn1ApAgAgJkKRn1ApAgAgJkKRn1ApAgAgJkKRn1ApAgAgJkKRn1ApAgAgJkKRHytFGzaYHTgQ76MBACChEIr8pEIFs0KFzAIBs40b4300AAAkFEKRn6SmmlWsmHGffkUAAIQhFPm1XxGhCACAMIQiv6GzNQAAURGK/IZh+QAAREUo8hsqRQAAREUo8hsqRQAAREUo8hsqRQAAREUo8hsqRQAAREUo8nOlSJM4AgAAh1Dk10rR7t1m6enxPhoAABIGochvSpQwK1064z79igAACCIU+RH9igAAyIRQ5EeMQAMAIBNCkR9RKQIAIBNCkR+xKCwAAJkQivxcKaL5DACAIEKRH1EpAgAgE0KRH9HRGgCATAhFfkRHawAAMiEU+blStHmz2b598T4aAAASQsKHot27d1u3bt2sVKlSVqlSJRs4cGDM13777bd21llnWbFixeyMM86wOXPmhD1fvnx5S0lJCbvt2LHDfOe448wKF864v359vI8GAICEkPChqHfv3jZ//nz76quvbMKECTZ48GCbOHFiptcp3Fx66aXWpk0bW7x4sfvZtm1b2759u3t+/fr1tnnzZlu+fLlt3LgxeCtZsqT5TqFCjEADAKAghSJViUaNGmVDhgyxevXqWcuWLa1Hjx42YsSITK+dNGmSFS9e3B577DGrUaOGqyiVLl3abZcff/zRKlSoYLVq1XIVI+/mW/QrAgCg4ISiefPm2f79+61p06bBbc2aNbO5c+daIBAIe+2XX35p5513nmsSE/3U+7wmtEWLFtnJJ5+cz2eQwBiBBgBAmD86liSm1atXu2pOkSJFgtuqVKlie/bscU1hoZUevfa0004Le79eu3DhwmCl6NChQ66J7YcffrAzzzzThg0bZieeeGLUz967d6+7edLT091PhTTdCrrUihVdIj64enXwfJLhvI6U368B5+/v8xe/XwO/n38yX4P9OTifhA5FCj9FixYN2+Y9VtNadl7rve6nn36yTZs22cMPP2wVK1a0J5980lq0aOFCkzpxRxo0aJANGDAg0/apU6daiRIlrKCrs2OH1TWzlXPn2vxp09y2aX/89DO/XwPO39/nL36/Bn4//2S8Brt27UqOUKRRZAo7obzqjfoPZee13uumTJliBw4cCD5+7bXXrHr16vbee+9Z586dM312nz59rGfPnmGVIr2+devWrq9SQVdo1SqzN9+0GmlpVqlVK/eXoFWrVpaWlmZ+pP+T8PM14Pz9ff7i92vg9/NP5muQ/kdLT4EPRVWrVnXNZPpFeb+gNWvWuABUrly5TK9dF9FpWK/VdtH7Q3/JqiLVrFnT1q5dG/Wz9Xxk5SnafgqsatXcj0Lr1wfPJ2nO7Sj4/Rpw/v4+f/H7NfD7+SfjNcjJuSR0R+tGjRpZ4cKFbfbs2cFts2bNsnPOOSfYodrTpEkT++yzz4IdsPXz888/d9vllFNOsZEjRwZfv3PnTluyZIkb1eZLDMkHAKDghCL13enatav16tXL9f2ZPn26DR8+3Lp37+6eVx8hrzmtffv2bk6i/v3728qVK91PtSNee+217nnNWaTh+jNnznTzGN14441ueL7mMzK/jz6LGMkHAIAfJXQokqFDh1r9+vWtcePG1qlTJ+vbt6916NDBPad5h7yJHNXP5/3337cPPvjADb3/8MMP3X1vckZ1nO7YsaNdd911brZr9T9Sf6LU1FTzdaVIy3z8/nu8jwYAgLhL6D5FolAzbtw4d4sUOVeRgtN3330XdT/qH6TZsHWDuyBmxx6bEYiYwBEAgMSvFCHvm9BSWP8MAABCka+x1AcAAEGEIj+jUgQAQBChyM8Ylg8AQBChyM+8ShGhCAAAQpGvUSkCACCIUORnVIoAAAgiFPkZlSIAAIIIRX7mVYq2brVCfyyXAgCAXxGK/Kxs2YyZrTXB9dat8T4aAADiilDkZykpwSa0Yqx/BgDwOUKR3/3RhFaMShEAwOcIRX73R6Wo6JYt8T4SAADiilDkd1SKAABwCEV+51WK6FMEAPA5QpHfeZUiQhEAwOcKx/sAEGcVKrgfpVautJRPPzVr0cIsNTX/Pv/gQbNZs8zWrcsIaM2b5+/n/3EMOveqM2dayjHH+O8acP7xPf8/jsHX14Dz59/BgwlwDSSAbNm2bVtAl0s/k8bkyYFAxYqBgP4YeLdq1TK259fn6/Pi9fmJcAx8vr8/PxGOgc/39+cnwjFMztvPz8n3N6HIr6FIf9hSUsL/EOqmbbrl9V+GeH9+IhwDn+/vz0+EY+Dz/f35iXAMk/P+83Py/U3zmR+pTHnvvRl/9CJ52267zezAgbwpX+rz77orfp+fCMfA5/v78xPhGPh8f39+IhzDwcN8viYYvu8+syuvzLemtBQlo3z5pAIuPT3dypQpY9u2bbPSpUtbgTZjRkZ7MQAAiW76dLMLL8yX728qRX6kjmzZUadOsCN2rtq40Wzx4vh9fiIcA5/v789PhGPg8/39+YlwDBuz+fnZ/c7KDUfdWOcTSdWnaPr0zO230W56XTJ+fiIcA5/v789PhGPg8/39+YlwDNPz5/Nz8v1N85kfm8/UjluzptmaNRl/5CKpHbdaNbMVK/KuHTmen58Ix8Dn+/vzE+EY+Hx/f34iHMPB/Pn8nHx/M3mjH+kP13PP/f8fulDe42efzbu/iPH+/EQ4Bj7f35+fCMfA5/v78xPhGFIT4BpEOqqalI8kVfNZVnNDVK8e37kp8vPzE+EY+Hx/f34iHAOf7+/PT4RjmJy3n0/zWR5IquazUAcP2oHp023ehx9ao0suscJ+nMXU79eA84/v+f9xDL6+Bpw//w4ezLtrwOgzZF9qqgUuuMDW7Nxpp19wQf7/RdTnHcVQy9w6Bl9fA84/vuf/xzH4+hpw/vw7mJoA14A+RQAAABkIRQAAAIQiAACADIQiAAAAQhEAAEAGQhEAAAChCAAAIAOhCAAAgFAEAACQgRmts8lbDUXThSeb/fv3265du9y5paWlmR/5/Rpw/v4+f/H7NfD7+SfzNUj/43s7O6uaEYqyafv27e5n9erV430oAADgCL7HtQZaVlgQNpsOHTpka9eutVKlSllKSoolE6Vohb1Vq1Yl12K3OeD3a8D5+/v8xe/XwO/nn8zXIBAIuEBUpUoVK1Qo615DVIqySReyWrVqlsz0lyCZ/iIcCb9fA87f3+cvfr8Gfj//ZL0Gh6sQeehoDQAAQCgCAADIQCiCFS1a1B5++GH306/8fg04f3+fv/j9Gvj9/KUo14CO1gAAAEKlCAAAgFAEAACQgVAEAABAKMKKFSvs8ssvd3M41KpVywYNGuQmqvSj22+/3S688ELzG3UrVOfKypUr27HHHmu33Xab7dmzx/zit99+s2uuucbKli1rJ5xwgvs74Bf79u2z+vXr24wZM4Lbli9fbi1atLBixYpZnTp17P333zc/nf/06dPt7LPPtpIlS1rDhg19d/6hS36cdtpp9sgjj5ifEIp8TH8hFIj0ZfDtt9/aP//5T3vmmWfspZdeMr/57LPPbOTIkeZHgwcPdr/7N954w/3j+Omnn/rqH8KbbrrJfv/9d/vyyy9twoQJ9vzzz9uYMWMs2e3du9euv/56+/HHH4Pb9D9EV111lfsfpMWLF9vdd99t1157rfufJz+c/7Jly6xt27Zu+4IFC+zOO++0du3a2Q8//GB+OP9QTz/9tLsGvqPRZ/CnmTNnBooWLRrYtWtXcNsTTzwRaNasWcBP9u7dG6hfv36gefPmgQsuuCDgJwcPHgxUqFAhMH78+OA23b/ooosCflGiRInAe++9F3zcs2fPwFVXXRVIZgsXLgw0atTI3fQ1MH36dLf9k08+cddjx44dwde2aNEi0L9//4Afzv/RRx8NnH/++WGvbdWqVaBfv34BP5y/Z8mSJYEqVaoETj311MDDDz8c8BMqRT5Wt25de++996x48eLBbVrXbefOneYnai4544wz7M9//rP5zcKFC23Tpk12xRVXBLfp/x7/97//mV/odz9x4kT3f87r16+3//73v9leEqCgmjlzpmsiU4U0lKplZ555ph1zzDHBbc2aNbM5c+aYH86/Y8eONnTo0LBtyfhvYqzzD+1KMGDAACtfvrz5DWuf+ViFChWsVatWwcf6Uhg9enTYF2Sy++mnn1zT0ffff28vvvii+c3PP//s/hzMmjXL+vXrZ1u3bnXNBQqKRYoUMT8YP368XXDBBa4PycGDB10/mgcffNCS2R133BF1++rVq61q1aph27SI5po1a8wP53/KKaeEPVbTkvoY/fWvfzU/nL+8+uqrrmvFzTff7P5u+A2hCI6+DFQh2Lx5s91///3mlw7G+j8i9Z+pWLGi+dGOHTvcrW/fvvbss8+6bbfccov78+A9TnZdunRxfWjefvttW7dunT355JO2ceNGO+mkk8xv1ME+cjZjPd69e7f5zYYNG1z/qj/96U+un5EfbNy40fr06eMqxaqQ+RGhCK5zZbdu3VxT2pQpU+z44483P3jllVfcCAuNtvKrwoUL265du1wA8kbeqeN1586dbdiwYVaoUHK3sH/xxRf2+eefuwqJ9+d++/bt7n8QVEXz2xeDRpypOTWUKsihTex+sGXLFldFV0hUWPbLn4P77rvPbrzxRmvQoIH5FaHI51QR+Mtf/mLvvvuuTZ482S666CLzi9dff92+++47Nwxd9A/ggQMH3Gi8+fPnu+HZyU7D8EVNRqF9zXQtVDVU01oyW7Vqles3Efo/AupjpNFWCgfJfv6R1HQWOdJKTWeRTWrJXiHSv4MKx5988omvzv311193zcjqUiCqIquf2TvvvOP+TfQDQpHP3Xvvvfavf/3LzcXhp0Ak6lwbOh+PqiX6B0BD09WPwg8aNWpkaWlpbkqGyy67LNiPolSpUr7oZHniiSe68KMO1pUqVQr2M1N/Ki8s+0mTJk1s4MCBrmOx19la/c38Mn+X/j249NJLXXOhOiFXq1bN/GRFxNQL1113nfsz4ZcuFUIo8jGNKPnHP/5hL7zwgp1++unBsnlqaqovvhC8KolHFSI1H9SsWdP8QueseXpUNj/uuONcQFKfAs3P4ocmA/UXadq0qauWKhR7ferUnKymRb9R+FGFVPMTaUJPNafPnTvXxo0bZ37w3HPPubl5NF9XaFOi7quCkuxqRvzbp/PWvxF+CofJ3WEAWVJbuWhkhZoJvJuaD+AfmqxQHUl107QEGon16KOPml+o6VhVsebNm1unTp3c6Dv1p/Ij9SFT5Xjp0qWuSVUhQU0nNWrUML/8m6g+VOeee27Yv4nJNvoMsaVosqIsngcAAPAFKkUAAACEIgAAgAyEIgAAAEIRgESQ3RmTNXeMliAAgLxAKAJw1DSEWSN0Pv744yN6v9578sknu+HQWXn88cfdaCBv5GR2LV++3M1BpbXdckrD9PXelStXRn1eE34myngVLVGiqRS0dA2AnCMUAThq8+bNc3NeaYbwnNKMyVp4UsuNaDLFrKpJY8eOdT81oVxOaEJCDbOOtSp4VqZNm+beO3z48KjPa1mE+vXru6HsHk0GqZnBj+Q2derUHB8jgNzhv9nJAGSL1sLTjNfVq1fP08/RRIFqEtMsyrHmyFIFSqt3K2xogdKWLVtGfZ1m4M3PKomWhNBxaxbsq6++2q0sPmLECLem3uLFi49on+np6VG3a84g7ftwa3aJJmTVzOxZ0TX10xIWQHYQigBEbW5q3769W/pClQtVMPLCqFGj3OSJsm3bNneL5tNPPw1OKKnJ9WIFjt9++y3bS7w88cQTmbaXKVPGLRCbXRUrVnTHr+ZDTfyo81FQ8qpKmjH766+/dvfHjBlj559/vtWuXds9VhOg1pZSpUluueUW9/6sVjDPbtBSk59uWVFwAxCO5jMAmeiLW5Wi33//3c30fCTNYoejBYjvuOMOt6TMr7/+6vrl/Pvf/3bPXXPNNe6xbmryuuuuu9yX+Msvv+y2acZlLUuipThUpfFe6y1keSQLIy9cuNAFjyNdHuOLL75wVTXtJzKQqPpz++23u+V0vI7iWk5ES6zklCpm3vnqpkCmYKawGLpdt19++cU9p/XsIp/z03I2QLZpRmsAiGbGjBmB4sWLB6pXrx7YuHGj2/bee++pV3GObnPnzg3b76RJkwIpKSmBokWLBj7++OOw584999xAampq4JdffnGP9blnn312oEuXLmGvmzx5stt3u3btoh77Bx98EKhTp467lSlTxr22WrVq7vGf//znsNeuWLHCPd+sWbPAU089FXyfblWqVHHPHXfcce7xnDlzYl6vBQsWuONetWqVe8+f/vQnt33EiBHucceOHYOvrVGjhtvmufnmm91jXZto+vXr555/9dVXw7brM7Td+/2EGjlypHvu4YcfjnnMAP4fzWcAYtI6aOqfor4ynTt3to8++sgtjKl1sUKpoqRmI60hVq5cubBO1GoiiqRK0NChQ61hw4ZuvbVQ99xzj+vcrOqNKkf9+vVzzU6RHZ3Vh+f666+P2ela1ZnI5qbVq1cHV0OP1uymxVB1HtGaqdRfRzd1CPeoP9FTTz3lmsBUXTv11FPddnUGnzRpkqtm6fy9fk7du3e3WHQuurb16tWL+RoAeSwkIAFAVFdccUWgfPnygR9//DHq888884yrSDzxxBNh26+88sqolSJRxSW0IhPtVrlyZff+UqVKHfa1f//732Mef8OGDd1+VOXy/PTTT4FDhw65+2+99ZZ7vm/fvpneO3HiRPdcr169Mj132223ueeKFSsWeOGFFzI9f+DAgcBVV13lXnPNNdeEPedVitatW+ceHzx40F0vbV+8eHHMSlH37t1zXKmLvN1+++0xrxXgZ1SKAByWqjRpaWlWpUqVLF+Xmpqa7X2q4pLdjsOatPFwr123bl3MilHk/EfPPPOM69Ojzs0vvfSS61gumispJ55//nm3gvzDDz/s5lpau3ZtsAO3KkS33XabG6qvqQYi+zupqqa+VMcff3zYdvWxyuo6Fy9ePKxSp32o8nXSSSdluv7quK4qmKp3+jxP5cqVc3SegF8QigAclr74s+J1HlZwyknn5MNNeqgRW+qM3LFjx8MOMY9l9uzZdujQIXdfAUWTG3bt2tVGjx7tOm5rMkhvYsbTTjvNNZHNnz/fHd/hFClSxPr27WtnnXWWXXnllW4+JI3U+8tf/uKaw/7zn/+4Ds0ffvhhMJSoM7c6RT/22GM2YMCAsAkly5Yt6yaoVDNaLGqiGzx4cPCxPvubb76xWbNmZQo7r7zyit16660usDGhI3B4hCIAh6Vh+WeeeWZYtSGU98VeunTpHO9bFZRnn3026nPeEP0pU6bEnBbg2muvdQEjlrfeeits5JZGvSlEqN/SOeec4yo7CiEKOAocbdq0sTlz5rgwFWvepEitW7e2cePGueqT7otCl6YReOihh8L6WV1++eVu/xoRppmyc4tGCUarFAHIPkIRgMNSZ2c1MT344IOuuSmSJlUUNSMNGTIkrKO1aM6jYsWK2Q033OD2EWrTpk2HbRrLqvksVrOZqFnpnXfeCT7u06ePDRo0yE0FoKCnztDqxK2mrqZNm7pj1PE3btzYdS5XeMouhbMWLVoEg6OCkCZczG2qdEWzbNmyXP8swG+YpwhAllRt0DxFagbSfYWTyJtXKVKfmtDt3sgz9XvR46wmV9RszZFz6aiyI2o+i3xO8ygdzsiRI90xe32FNFJNkyyqSqQqjcLPnXfe6Z5TM5rXHKXX6JzV/JUTXiBSsFJ4iXZTlUg0yiza87Ga7bTGmkRWgzQ5ZOS1ibzRdAZkD6EIwGGXg9DweA3P15dr5BeumoH0Ra0lQTSkXf2KVq1aFfXLWcP7c8KbBFGVopzSsHj18dGs3KoCee677z436aM3IeXcuXPdTwWgb7/9Nrj0iNcEll2qWKkJ63//+5+VKlXKLaERevMqPF6/K1WlRMP2Q1/nhbNY/bbUzKeK15Gsq6aJLgHERvMZgCxNmDDB/bzkkkuiPq+gpNCk5xWO1FF42LBh7nY0NEN0//79g32K1DeoQ4cOrklMVRPN1iyFCkX/fzt1gFZlSv2NvNd6laAlS5ZYrVq1XAdrfU61atXcHEbq+Ky5kVRBatCggV188cXZPl6FIc2+rb5I6kekGaxDF8xV/yR9tq6RqkUKbD179rSrrroqy+U9ItdEO+aYY1wAO5K11dTBG0BsVIoAxKRJGd9//303bDxykkXvy179cvRFrxCgCouGjKvZypso8UgsWrTIdUhWtcfTpUsX++CDD1w4UIdur5pz3nnnRV0rTf15FHYUPCIpEHmVIFWw1EFa65SpSU5LiIj6E2l5kezyVrePdjya4FG6desW3KbApeMbP368q6wdjiaV9PoqaVSejlvXXlQNC13uxLup+dILs/r9KegBiI1QBCAmNXep2UZ9bCL7sqiDdLt27dxwd81CrWH7WiD1b3/7m/syVj+g7Cw6qoCjZiNvGLpGh5177rmu6UxNS95rVOXQsPfXX3/dDXdX3xuFDYWlSKr0XHfddS48lChRIurnqq+RgpOa0nR+mrFbQUKjyLxmquzSzNYa7q8qTuQM2wpab775putvpM/wqMKlpjxdX82I7U0bEIs3bYCClEcd2HW91dH9oosuCqseKVjqOmo6AAU+XdeczCMF+FK8Z48EkJh+//33QNmyZQNFihQJrFy5Muy53bt3B5o3b+5mRz799NMDO3fuDD63Z8+e4AzSd955Z3DW6MPZtm1b4J577gnOuty+ffvAa6+9Flwz7Mknnww+p7XLtM5YdnnrioXOaO3NEK3nZPXq1W49Np1Pdme09owZMybT2maiY9Q1DF3TrHHjxu6xZgffv39/4IwzznCP77vvvpj737t3r5s1u2TJkpmup2bC1gzV2kfp0qXd8T722GPu9dqmWbLT09Ozfa0APyMUAYjqrrvucl+q+hlKX+Ze6Kldu3ZgzZo1md67bNmyQLly5dxrOnTo4EJULApUCjxacFWv1yKxWi5EX/YKEqFhQyHJCxlaNPaGG24IfPvttzkORV9++WWgcOHC7rNCA59eN3369ByFIh1n/fr13Wv+85//BLfrs7Q8SWjwigxF8sMPPwTPqXPnzlGv1f/+9z/3/EUXXRTzHHv37h22lIeuj5YeyW4oBUAoAhCFvtz1xVqhQoXA+vXr3bbt27cHhg0bFjjmmGOCK8D/9ttvMffx9ddfB7/szzrrLPfYowrJ1KlTA7feemswPOnWsmVLtyaZJzIUydq1awPXXnutq+p471O15emnn465NltoKNJaYwpzejx48ODDXovDhaJx48a55+vWresCyPfffx+4/PLLg8fWo0ePsGASGYoir5XWPtNnhlJY0nNaYy6UApTOSc8r4Ok1hQoVCn629tWnT5/AzJkzA/v27TvsuQJ+RygCkMny5csDTZo0Cbz77rvuC/2BBx4IlClTJvilqy96Nekcjr74tVir3qcQo8rO1q1bw5rJtD8tOKvqTaRooSh037fcckswpOnWtWvXLEPRiBEjAieffLK7f+mll2arijJ27NiYoUgBzQt1r776ajBQ6lwVSCZMmJDpPdFCkSxatMhdcz13xx13BLcvXLjQVX0UembMmOFCmIJOixYtAiVKlAiee8WKFd2CtqrcKQRdffXVgbS0tODzeu25557rmjQVILUvAOEIRQBiNgt51AyjL/rLLrssMG/evBztZ9euXS5UqY+Lwo/2q/5Duv/cc88FV4mPJqtQFFotURDRivWx9uWFohdffDFQvXp1Fyh27NgRc58KLAoZp5xyiuuno/cOGDAg0+umTZvmQpmCTuj1+vjjj13fqmhihSLRPt54443AgQMHgtvuvfde9/qePXsGZs2aFVYJOuGEE9y5TZkyxVXfIm3ZsiUwevRoV1mrVKlSWPPa5MmTY54/4Fcp+k+8O3sDSHwaYh868imnNEJKo7OyWuw00ttvv+2WzziaBWHllltucXMBaSRY/fr13RQDmjogKxpa//nnn7v7mhFbs2BHWxhXo7w0ciy7w901Os1b+yzWem6hNAfUiy++6JYd0Ug6zdekUWSa96h27dqWE1qqZcGCBW75FW8mbwD/j1AEAADAPEUAAAAZCEUAAACEIgAAgAyEIgAAAEIRAABABkIRAAAAoQgAACADoQgAAIBQBAAAkIFQBAAAQCgCAADIQCgCAAAGs/8DKDV24t9nCUQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import tree\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "X,y = datasets.load_iris(return_X_y=True)\n",
    "\n",
    "# 随机拆分\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,random_state = 256)\n",
    "depth = np.arange(1,16)\n",
    "err = []\n",
    "for d in depth:\n",
    "    model = DecisionTreeClassifier(criterion='entropy',max_depth=d)\n",
    "    model.fit(X_train,y_train)\n",
    "    score = model.score(X_test,y_test)\n",
    "    err.append(1 - score)\n",
    "    print('错误率为%0.3f%%' % (100 * (1 - score)))\n",
    "plt.rcParams['font.family'] = 'Arial Unicode MS'\n",
    "plt.plot(depth,err,'ro-')\n",
    "plt.xlabel('决策树深度',fontsize = 18)\n",
    "plt.ylabel('错误率',fontsize = 18)\n",
    "plt.title('筛选合适决策树深度')\n",
    "plt.grid()\n",
    "plt.savefig('./14-筛选超参数.png',dpi = 200)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 决策树"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 测试"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "问题：图片是二叉树吗？\n",
    "\n",
    "决策树是标准的二叉树，每个节点只有两个分支~\n",
    "\n",
    "上面那棵树中，属性：绿色的节点（年龄、长相、收入、是否是公务员）\n",
    "属性叫做，data，数据，一般使用X表示\n",
    "跟属性对应，目标值（橘色节点），一般使用y表示\n",
    "构建这棵树时，先后顺序，每个人，标准不同，树结构不同\n",
    "计算机，构建树，标准一致的，构建出来的树，一致\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日志密度</th>\n",
       "      <th>好友密度</th>\n",
       "      <th>真实头像</th>\n",
       "      <th>真实用户</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s</td>\n",
       "      <td>s</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s</td>\n",
       "      <td>l</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>l</td>\n",
       "      <td>m</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>l</td>\n",
       "      <td>m</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>m</td>\n",
       "      <td>l</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>m</td>\n",
       "      <td>s</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>l</td>\n",
       "      <td>m</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>m</td>\n",
       "      <td>s</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>s</td>\n",
       "      <td>s</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  日志密度 好友密度 真实头像 真实用户\n",
       "0    s    s    N    N\n",
       "1    s    l    Y    Y\n",
       "2    l    m    Y    Y\n",
       "3    m    m    Y    Y\n",
       "4    l    m    Y    Y\n",
       "5    m    l    N    Y\n",
       "6    m    s    Y    N\n",
       "7    l    m    Y    Y\n",
       "8    m    s    Y    Y\n",
       "9    s    s    Y    N"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "y = np.array(list('NYYYYYNYYN'))\n",
    "\n",
    "X = pd.DataFrame({'日志密度':list('sslmlmmlms'),\n",
    "    '好友密度':list('slmmmlsmss'),\n",
    "    '真实头像':list('NYYYYNYYYY'),\n",
    "    '真实用户':y\n",
    "})\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日志密度</th>\n",
       "      <th>好友密度</th>\n",
       "      <th>真实头像</th>\n",
       "      <th>真实用户</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   日志密度  好友密度  真实头像 真实用户\n",
       "0     0     0     0    N\n",
       "1     0     2     1    Y\n",
       "2     2     1     1    Y\n",
       "3     1     1     1    Y\n",
       "4     2     1     1    Y\n",
       "5     1     2     0    Y\n",
       "6     1     0     1    N\n",
       "7     2     1     1    Y\n",
       "8     1     0     1    Y\n",
       "9     0     0     1    N"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['日志密度'] = X['日志密度'].map({'s':0,'m':1,'l':2})\n",
    "X['好友密度'] = X['好友密度'].map({'s':0,'m':1,'l':2})\n",
    "X['真实头像'] = X['真实头像'].map({'N':0,'Y':1})\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family 'STKaiti' not found.\n",
      "findfont: Font family 'STKaiti' not found.\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/sklearn/tree/_export.py:673: UserWarning: Glyph 22909 (\\N{CJK UNIFIED IDEOGRAPH-597D}) missing from font(s) DejaVu Sans.\n",
      "  ann.update_bbox_position_size(renderer)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/sklearn/tree/_export.py:673: UserWarning: Glyph 21451 (\\N{CJK UNIFIED IDEOGRAPH-53CB}) missing from font(s) DejaVu Sans.\n",
      "  ann.update_bbox_position_size(renderer)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/sklearn/tree/_export.py:673: UserWarning: Glyph 23494 (\\N{CJK UNIFIED IDEOGRAPH-5BC6}) missing from font(s) DejaVu Sans.\n",
      "  ann.update_bbox_position_size(renderer)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/sklearn/tree/_export.py:673: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from font(s) DejaVu Sans.\n",
      "  ann.update_bbox_position_size(renderer)\n",
      "findfont: Font family 'STKaiti' not found.\n",
      "findfont: Font family 'STKaiti' not found.\n",
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      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/sklearn/tree/_export.py:673: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from font(s) DejaVu Sans.\n",
      "  ann.update_bbox_position_size(renderer)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/sklearn/tree/_export.py:673: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from font(s) DejaVu Sans.\n",
      "  ann.update_bbox_position_size(renderer)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/sklearn/tree/_export.py:673: UserWarning: Glyph 22836 (\\N{CJK UNIFIED IDEOGRAPH-5934}) missing from font(s) DejaVu Sans.\n",
      "  ann.update_bbox_position_size(renderer)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/sklearn/tree/_export.py:673: UserWarning: Glyph 20687 (\\N{CJK UNIFIED IDEOGRAPH-50CF}) missing from font(s) DejaVu Sans.\n",
      "  ann.update_bbox_position_size(renderer)\n",
      "findfont: Font family 'STKaiti' not found.\n",
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      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/events.py:82: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from font(s) DejaVu Sans.\n",
      "  func(*args, **kwargs)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/events.py:82: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from font(s) DejaVu Sans.\n",
      "  func(*args, **kwargs)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/events.py:82: UserWarning: Glyph 22836 (\\N{CJK UNIFIED IDEOGRAPH-5934}) missing from font(s) DejaVu Sans.\n",
      "  func(*args, **kwargs)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/events.py:82: UserWarning: Glyph 20687 (\\N{CJK UNIFIED IDEOGRAPH-50CF}) missing from font(s) DejaVu Sans.\n",
      "  func(*args, **kwargs)\n",
      "findfont: Font family 'STKaiti' not found.\n",
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      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/events.py:82: UserWarning: Glyph 22909 (\\N{CJK UNIFIED IDEOGRAPH-597D}) missing from font(s) DejaVu Sans.\n",
      "  func(*args, **kwargs)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/events.py:82: UserWarning: Glyph 21451 (\\N{CJK UNIFIED IDEOGRAPH-53CB}) missing from font(s) DejaVu Sans.\n",
      "  func(*args, **kwargs)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/events.py:82: UserWarning: Glyph 23494 (\\N{CJK UNIFIED IDEOGRAPH-5BC6}) missing from font(s) DejaVu Sans.\n",
      "  func(*args, **kwargs)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/events.py:82: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from font(s) DejaVu Sans.\n",
      "  func(*args, **kwargs)\n",
      "findfont: Font family 'STKaiti' not found.\n",
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      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 22836 (\\N{CJK UNIFIED IDEOGRAPH-5934}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 20687 (\\N{CJK UNIFIED IDEOGRAPH-50CF}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Font family 'STKaiti' not found.\n",
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      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 22909 (\\N{CJK UNIFIED IDEOGRAPH-597D}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 21451 (\\N{CJK UNIFIED IDEOGRAPH-53CB}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 23494 (\\N{CJK UNIFIED IDEOGRAPH-5BC6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/xindors/Documents/workspace/hetonghai-projects/artificial-intelligence-code/.venv/lib/python3.13/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Font family 'STKaiti' not found.\n",
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      "findfont: Font family 'STKaiti' not found.\n",
      "findfont: Font family 'STKaiti' not found.\n",
      "findfont: Font family 'STKaiti' not found.\n",
      "findfont: Font family 'STKaiti' not found.\n",
      "findfont: Font family 'STKaiti' not found.\n",
      "findfont: Font family 'STKaiti' not found.\n",
      "findfont: Font family 'STKaiti' not found.\n",
      "findfont: Font family 'STKaiti' not found.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 模型可视化\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import tree\n",
    "\n",
    "model = DecisionTreeClassifier(max_depth=3, random_state=0)\n",
    "model.fit(X, y)\n",
    "plt.rcParams['font.family'] = 'STKaiti'\n",
    "plt.figure(figsize=(12, 16))\n",
    "fn = X.columns\n",
    "_ = tree.plot_tree(model,filled=True,feature_names = fn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Account.png'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 另外一种可视化方法\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import graphviz\n",
    "from sklearn import tree\n",
    "\n",
    "model = DecisionTreeClassifier(criterion='entropy')\n",
    "model.fit(X, y)\n",
    "\n",
    "dot_data =  tree.export_graphviz(model, out_file=None, feature_names=X.columns, class_names=np.unique(y), filled=True, rounded=True, special_characters=True)\n",
    "\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph.render(\"Account\",format=\"png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cmex10',\n",
       " 'STIXGeneral',\n",
       " 'DejaVu Sans',\n",
       " 'DejaVu Sans Mono',\n",
       " 'STIXSizeOneSym',\n",
       " 'cmr10',\n",
       " 'DejaVu Sans',\n",
       " 'DejaVu Sans Mono',\n",
       " 'DejaVu Sans Mono',\n",
       " 'cmsy10',\n",
       " 'STIXNonUnicode',\n",
       " 'DejaVu Serif Display',\n",
       " 'DejaVu Sans Display',\n",
       " 'DejaVu Sans',\n",
       " 'STIXSizeFourSym',\n",
       " 'cmtt10',\n",
       " 'STIXSizeFourSym',\n",
       " 'DejaVu Sans Mono',\n",
       " 'STIXSizeTwoSym',\n",
       " 'STIXSizeTwoSym',\n",
       " 'cmmi10',\n",
       " 'STIXGeneral',\n",
       " 'DejaVu Sans',\n",
       " 'STIXSizeOneSym',\n",
       " 'STIXGeneral',\n",
       " 'STIXSizeThreeSym',\n",
       " 'DejaVu Serif',\n",
       " 'STIXNonUnicode',\n",
       " 'STIXGeneral',\n",
       " 'cmb10',\n",
       " 'DejaVu Serif',\n",
       " 'DejaVu Serif',\n",
       " 'STIXSizeThreeSym',\n",
       " 'cmss10',\n",
       " 'STIXNonUnicode',\n",
       " 'STIXNonUnicode',\n",
       " 'STIXSizeFiveSym',\n",
       " 'DejaVu Serif',\n",
       " 'Times New Roman',\n",
       " 'Noto Sans Rejang',\n",
       " 'Devanagari MT',\n",
       " 'Telugu Sangam MN',\n",
       " 'Cochin',\n",
       " 'Noto Sans Linear B',\n",
       " 'DIN Condensed',\n",
       " 'Silom',\n",
       " 'Noto Sans Batak',\n",
       " 'Iowan Old Style',\n",
       " 'Times',\n",
       " 'Noto Sans NKo',\n",
       " 'Herculanum',\n",
       " 'Noto Sans Tagbanwa',\n",
       " 'STIXSizeTwoSym',\n",
       " 'Apple Braille',\n",
       " 'Courier New',\n",
       " 'Hack Nerd Font',\n",
       " 'Trebuchet MS',\n",
       " 'Noto Sans Tifinagh',\n",
       " 'LiSong Pro',\n",
       " 'Noto Sans Thaana',\n",
       " 'Noto Sans Brahmi',\n",
       " 'Hei',\n",
       " 'Noto Sans Glagolitic',\n",
       " 'Malayalam Sangam MN',\n",
       " 'Monaco',\n",
       " 'Tsukushi B Round Gothic',\n",
       " 'Ayuthaya',\n",
       " 'Noto Sans Imperial Aramaic',\n",
       " 'Noto Sans Masaram Gondi',\n",
       " 'STIXNonUnicode',\n",
       " 'AppleMyungjo',\n",
       " 'STIXGeneral',\n",
       " 'PSL Ornanong Pro',\n",
       " 'Noto Sans Lepcha',\n",
       " 'Noto Sans Chakma',\n",
       " '.SF Georgian',\n",
       " 'Bangla Sangam MN',\n",
       " 'Khmer Sangam MN',\n",
       " 'Noto Sans Lycian',\n",
       " 'STIXIntegralsUpSm',\n",
       " 'Noto Sans Vai',\n",
       " 'Noto Sans Mongolian',\n",
       " 'STIXIntegralsSm',\n",
       " 'Sinhala Sangam MN',\n",
       " 'PT Mono',\n",
       " 'PingFang HK',\n",
       " 'Chalkboard',\n",
       " '.SF Compact Rounded',\n",
       " 'Gujarati MT',\n",
       " 'Yuppy TC',\n",
       " 'Verdana',\n",
       " 'Lao Sangam MN',\n",
       " 'Noto Sans Psalter Pahlavi',\n",
       " 'Copperplate',\n",
       " 'STIXNonUnicode',\n",
       " 'Gurmukhi Sangam MN',\n",
       " 'STIX Two Text',\n",
       " 'Arial Narrow',\n",
       " 'Noto Sans Kaithi',\n",
       " 'Optima',\n",
       " 'Noto Sans Syriac',\n",
       " '.SF Armenian',\n",
       " 'Noto Sans Javanese',\n",
       " 'YuKyokasho Yoko',\n",
       " 'System Font',\n",
       " 'Noto Sans Myanmar',\n",
       " 'Al Tarikh',\n",
       " 'Corsiva Hebrew',\n",
       " 'Hiragino Sans',\n",
       " 'Mishafi Gold',\n",
       " 'Kai',\n",
       " 'BIZ UDMincho',\n",
       " 'Noto Sans Bamum',\n",
       " 'BiauKaiHK',\n",
       " 'Noto Sans Pau Cin Hau',\n",
       " 'Myanmar Sangam MN',\n",
       " 'STFangsong',\n",
       " 'Noto Sans Linear A',\n",
       " 'Arial',\n",
       " 'Hack Nerd Font Mono',\n",
       " 'Big Caslon',\n",
       " 'Times New Roman',\n",
       " 'YuMincho',\n",
       " 'Noto Sans Sharada',\n",
       " '.SF Arabic',\n",
       " 'Noto Serif Hmong Nyiakeng',\n",
       " 'Apple Symbols',\n",
       " 'Arial Narrow',\n",
       " 'Lucida Grande',\n",
       " 'Kohinoor Telugu',\n",
       " 'Lao MN',\n",
       " 'Noto Sans Osmanya',\n",
       " 'Noto Sans Pahawh Hmong',\n",
       " 'PT Serif',\n",
       " 'Noto Sans Elbasan',\n",
       " 'Noto Sans PhagsPa',\n",
       " 'Hack Nerd Font Propo',\n",
       " 'Futura',\n",
       " 'KufiStandardGK',\n",
       " 'Mukta Mahee',\n",
       " 'STIXIntegralsUpSm',\n",
       " '.SF Camera',\n",
       " 'GungSeo',\n",
       " 'Wingdings 3',\n",
       " 'Noto Sans Hanunoo',\n",
       " 'DIN Alternate',\n",
       " 'Trebuchet MS',\n",
       " 'Nanum Gothic',\n",
       " 'Noto Sans Tirhuta',\n",
       " 'Noto Sans Armenian',\n",
       " 'Sana',\n",
       " 'Libian SC',\n",
       " 'STIXSizeOneSym',\n",
       " 'Noto Sans Takri',\n",
       " 'Toppan Bunkyu Mincho',\n",
       " '.SF Hebrew',\n",
       " 'STIXIntegralsUp',\n",
       " 'BM Dohyeon',\n",
       " 'BM Hanna Pro',\n",
       " 'Kannada Sangam MN',\n",
       " 'Arial Black',\n",
       " 'STIXIntegralsD',\n",
       " 'Kaiti SC',\n",
       " 'Bodoni 72 Oldstyle',\n",
       " 'Noto Sans Egyptian Hieroglyphs',\n",
       " 'Georgia',\n",
       " 'Noto Nastaliq Urdu',\n",
       " 'Gill Sans',\n",
       " 'Noto Sans Coptic',\n",
       " 'Apple LiSung',\n",
       " 'STIXIntegralsUpD',\n",
       " 'STIXGeneral',\n",
       " 'Apple Chancery',\n",
       " 'Noto Sans Warang Citi',\n",
       " 'Bradley Hand',\n",
       " 'Krungthep',\n",
       " 'Hiragino Sans',\n",
       " 'Palatino',\n",
       " 'Hiragino Sans CNS',\n",
       " 'Hack Nerd Font',\n",
       " 'Diwan Thuluth',\n",
       " 'Avenir Next',\n",
       " '.Aqua Kana',\n",
       " 'Telugu MN',\n",
       " 'Noto Sans Ugaritic',\n",
       " 'Noto Sans Hanifi Rohingya',\n",
       " 'Chalkboard SE',\n",
       " 'Gujarati Sangam MN',\n",
       " 'Apple Braille',\n",
       " '.ThonburiUI',\n",
       " 'Noto Sans Old Persian',\n",
       " 'Comic Sans MS',\n",
       " 'Tahoma',\n",
       " 'Oriya MN',\n",
       " 'Arial',\n",
       " 'STIXIntegralsSm',\n",
       " 'STIXNonUnicode',\n",
       " 'Skia',\n",
       " 'Noto Sans Old North Arabian',\n",
       " 'Kailasa',\n",
       " 'STIXVariants',\n",
       " 'STIXIntegralsUp',\n",
       " 'YuGothic',\n",
       " 'Noto Sans Lydian',\n",
       " 'Hannotate SC',\n",
       " 'Yuppy SC',\n",
       " 'Hack Nerd Font',\n",
       " 'BM Kirang Haerang',\n",
       " 'STIXVariants',\n",
       " 'Plantagenet Cherokee',\n",
       " 'Noto Sans Canadian Aboriginal',\n",
       " 'Tahoma',\n",
       " 'Microsoft Sans Serif',\n",
       " 'SignPainter',\n",
       " '.SF Armenian Rounded',\n",
       " 'Mishafi',\n",
       " 'Noto Sans Adlam',\n",
       " 'Al Bayan',\n",
       " 'Baskerville',\n",
       " 'Noto Sans Gunjala Gondi',\n",
       " 'Hiragino Sans',\n",
       " 'Noto Sans Modi',\n",
       " 'Trebuchet MS',\n",
       " 'Hiragino Sans',\n",
       " 'Noto Serif Myanmar',\n",
       " 'Kohinoor Bangla',\n",
       " 'Farah',\n",
       " 'Bodoni Ornaments',\n",
       " 'Symbol',\n",
       " 'Apple SD Gothic Neo',\n",
       " 'Hack Nerd Font Propo',\n",
       " 'Hoefler Text',\n",
       " '.New York',\n",
       " 'American Typewriter',\n",
       " 'Noto Sans Hatran',\n",
       " 'Noto Sans Tai Tham',\n",
       " 'Wawati TC',\n",
       " 'Wingdings 2',\n",
       " 'Courier',\n",
       " 'Noto Sans Mende Kikakui',\n",
       " 'Noto Sans Meetei Mayek',\n",
       " 'Trebuchet MS',\n",
       " 'InaiMathi',\n",
       " 'Hiragino Sans GB',\n",
       " 'Noto Sans Nabataean',\n",
       " 'Lantinghei SC',\n",
       " 'System Font',\n",
       " 'Mshtakan',\n",
       " 'Proxima Nova',\n",
       " 'Toppan Bunkyu Gothic',\n",
       " 'Noto Sans Gothic',\n",
       " 'Hiragino Maru Gothic Pro',\n",
       " 'PCMyungjo',\n",
       " 'Superclarendon',\n",
       " 'Apple LiGothic',\n",
       " 'LingWai SC',\n",
       " 'Hiragino Mincho ProN',\n",
       " 'Zapf Dingbats',\n",
       " 'Osaka',\n",
       " 'Noto Sans Bhaiksuki',\n",
       " 'Wawati SC',\n",
       " 'Noteworthy',\n",
       " 'Hiragino Sans',\n",
       " 'Helvetica',\n",
       " 'Noto Sans Old Italic',\n",
       " 'STIX Two Text',\n",
       " 'Noto Sans Kharoshthi',\n",
       " 'Arial Unicode MS',\n",
       " 'Comic Sans MS',\n",
       " 'Xingkai SC',\n",
       " 'Noto Sans Inscriptional Parthian',\n",
       " 'STIXNonUnicode',\n",
       " 'Geneva',\n",
       " 'Noto Sans Mro',\n",
       " 'Noto Sans Saurashtra',\n",
       " 'Noto Sans Avestan',\n",
       " 'Verdana',\n",
       " 'HanziPen SC',\n",
       " 'Noto Sans New Tai Lue',\n",
       " 'Publico Text',\n",
       " 'Noto Sans Syloti Nagri',\n",
       " 'Arial',\n",
       " 'Noto Sans Kayah Li',\n",
       " 'Nanum Myeongjo',\n",
       " 'Noto Sans Old South Arabian',\n",
       " 'Savoye LET',\n",
       " 'HeadLineA',\n",
       " 'Kohinoor Devanagari',\n",
       " '.Keyboard',\n",
       " '.SF Hebrew Rounded',\n",
       " 'Arial Rounded MT Bold',\n",
       " 'Gurmukhi MT',\n",
       " 'Hack Nerd Font',\n",
       " 'STIXSizeTwoSym',\n",
       " 'Myanmar MN',\n",
       " 'Phosphate',\n",
       " 'Verdana',\n",
       " 'Times New Roman',\n",
       " 'Noto Sans Oriya',\n",
       " 'Bodoni 72 Smallcaps',\n",
       " 'STIXSizeFourSym',\n",
       " 'Noto Sans Cuneiform',\n",
       " 'Georgia',\n",
       " 'Noto Sans Inscriptional Pahlavi',\n",
       " 'Chalkduster',\n",
       " 'Georgia',\n",
       " 'Noto Sans Newa',\n",
       " 'BM Hanna 11yrs Old',\n",
       " 'LingWai TC',\n",
       " 'Sathu',\n",
       " 'Impact',\n",
       " 'Hack Nerd Font Propo',\n",
       " 'Marker Felt',\n",
       " 'Noto Sans Old Turkic',\n",
       " 'Devanagari Sangam MN',\n",
       " 'Noto Sans Marchen',\n",
       " 'Noto Sans Tai Le',\n",
       " 'Apple Braille',\n",
       " 'STHeiti',\n",
       " 'Khmer MN',\n",
       " 'Papyrus',\n",
       " 'Courier New',\n",
       " 'Kohinoor Gujarati',\n",
       " 'Shree Devanagari 714',\n",
       " 'Diwan Kufi',\n",
       " 'Times New Roman',\n",
       " 'Noto Sans Old Permic',\n",
       " '.SF Arabic Rounded',\n",
       " 'YuGothic',\n",
       " 'BM Hanna Air',\n",
       " 'ITF Devanagari',\n",
       " 'Noto Sans Cham',\n",
       " 'Sukhumvit Set',\n",
       " 'Kannada MN',\n",
       " 'Noto Sans Caucasian Albanian',\n",
       " 'Noto Sans Ol Chiki',\n",
       " 'Zapfino',\n",
       " 'Didot',\n",
       " 'Wingdings',\n",
       " 'Hiragino Sans',\n",
       " 'Tamil Sangam MN',\n",
       " 'Sinhala MN',\n",
       " 'Noto Sans Wancho',\n",
       " 'Hack Nerd Font Propo',\n",
       " 'Noto Sans Lisu',\n",
       " 'Noto Sans Carian',\n",
       " 'AppleGothic',\n",
       " 'Noto Serif Ahom',\n",
       " 'STIXGeneral',\n",
       " 'Helvetica Neue',\n",
       " 'Noto Sans Cypriot',\n",
       " 'STIXIntegralsUpD',\n",
       " 'Noto Sans Meroitic',\n",
       " '.SF Georgian Rounded',\n",
       " 'LiHei Pro',\n",
       " 'Beirut',\n",
       " 'Noto Sans Bassa Vah',\n",
       " 'Avenir',\n",
       " 'Athelas',\n",
       " '.New York',\n",
       " 'Hiragino Sans',\n",
       " 'Kefa',\n",
       " 'Canela Text',\n",
       " 'Party LET',\n",
       " 'Luminari',\n",
       " 'DecoType Naskh',\n",
       " 'Arial Narrow',\n",
       " 'Academy Engraved LET',\n",
       " 'Oriya Sangam MN',\n",
       " 'Songti SC',\n",
       " 'Thonburi',\n",
       " 'Hack Nerd Font Mono',\n",
       " 'Muna',\n",
       " 'PilGi',\n",
       " 'Noto Sans Osage',\n",
       " 'Heiti TC',\n",
       " 'STHeiti',\n",
       " 'PT Serif Caption',\n",
       " 'Noto Sans Yi',\n",
       " 'Baghdad',\n",
       " 'Nadeem',\n",
       " 'Gurmukhi MN',\n",
       " 'Al Nile',\n",
       " 'Snell Roundhand',\n",
       " 'Noto Sans Siddham',\n",
       " 'Waseem',\n",
       " 'Nanum Brush Script',\n",
       " 'BM Jua',\n",
       " 'Verdana',\n",
       " 'Toppan Bunkyu Midashi Mincho',\n",
       " 'Yuanti SC',\n",
       " 'Seravek',\n",
       " 'BIZ UDGothic',\n",
       " 'Farisi',\n",
       " 'Tamil MN',\n",
       " 'Noto Sans Manichaean',\n",
       " 'STIXSizeThreeSym',\n",
       " 'Marion',\n",
       " 'Hiragino Sans',\n",
       " 'Galvji',\n",
       " 'Courier New',\n",
       " 'Euphemia UCAS',\n",
       " 'Georgia',\n",
       " 'Malayalam MN',\n",
       " 'Osaka',\n",
       " 'Arial Unicode MS',\n",
       " 'STIXSizeFiveSym',\n",
       " 'STIXIntegralsD',\n",
       " 'Toppan Bunkyu Midashi Gothic',\n",
       " 'Damascus',\n",
       " 'Noto Sans Limbu',\n",
       " 'Noto Sans Miao',\n",
       " 'STIXSizeOneSym',\n",
       " 'Rockwell',\n",
       " 'Apple Braille',\n",
       " 'Heiti TC',\n",
       " 'Avenir Next Condensed',\n",
       " 'Noto Sans Duployan',\n",
       " 'Noto Sans Buginese',\n",
       " 'Noto Sans Palmyrene',\n",
       " 'Arial Hebrew',\n",
       " 'Noto Sans Multani',\n",
       " 'Tsukushi A Round Gothic',\n",
       " 'Hack Nerd Font Mono',\n",
       " 'Noto Sans Sora Sompeng',\n",
       " 'Noto Serif Yezidi',\n",
       " 'Kokonor',\n",
       " 'Noto Sans Buhid',\n",
       " 'Menlo',\n",
       " 'Webdings',\n",
       " 'Hiragino Sans',\n",
       " 'Hoefler Text',\n",
       " 'Noto Sans Samaritan',\n",
       " '.SF NS Mono',\n",
       " 'Apple Braille',\n",
       " 'STIXGeneral',\n",
       " 'Hiragino Sans',\n",
       " 'Geeza Pro',\n",
       " 'New Peninim MT',\n",
       " 'Noto Sans Tai Viet',\n",
       " 'BM Yeonsung',\n",
       " 'Noto Sans Khudawadi',\n",
       " 'STIX Two Math',\n",
       " 'Noto Sans Sundanese',\n",
       " 'Trattatello',\n",
       " 'Noto Serif Balinese',\n",
       " 'Courier New',\n",
       " 'SimSong',\n",
       " 'Noto Sans Khojki',\n",
       " 'Brush Script MT',\n",
       " 'Raanana',\n",
       " 'Arial',\n",
       " 'Bangla MN',\n",
       " 'STIXSizeThreeSym',\n",
       " 'Noto Sans Old Hungarian',\n",
       " 'STIXSizeFourSym',\n",
       " 'Noto Sans Mandaic',\n",
       " 'Klee',\n",
       " 'Arial Narrow',\n",
       " 'Andale Mono',\n",
       " 'Noto Sans Mahajani',\n",
       " 'Charter',\n",
       " 'PT Sans',\n",
       " 'Baoli SC',\n",
       " 'Noto Sans Tagalog',\n",
       " '.SF Soft Numeric',\n",
       " 'Hack Nerd Font Mono',\n",
       " 'Noto Sans Phoenician',\n",
       " 'Noto Sans Kannada',\n",
       " 'Bodoni 72',\n",
       " '.SF NS Rounded']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看字体\n",
    "from matplotlib import font_manager\n",
    "fm = font_manager.FontManager()\n",
    "[font.name for font in fm.ttflist ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 信息熵\n",
    "\n",
    "* $ H(x) = -\\sum_{i=1}^n p(x_i) \\log_2 p(x_i) $ <br>\n",
    "  \n",
    "* $ H(x) = \\sum_{i=1}^n p(x_i) \\log_2 \\frac{1}{p(x_i)} $ <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 分裂条件计算\n",
    "\n",
    "X['真实用户'] = y\n",
    "X\n",
    "s = X['真实用户']\n",
    "p = s.value_counts()/s.size\n",
    "h =(p*np.log2(1/p)).sum() # 计算信息熵"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 信息增益\n",
    "\n",
    "信息增益是知道了某个条件后，时间的不确定性下降的程度。写作 $g(X,Y)。 它的计算方式为熵减去条件熵，如下:\n",
    "$$ g(X,Y) = H(Y) - H(Y|X) $$\n",
    "表示的是，知道某个条件后，原来事件不确定性降低的幅度。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 手动计算信息熵\n",
    "\n",
    "数据整合"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 计算未分类的信息熵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日志密度</th>\n",
       "      <th>好友密度</th>\n",
       "      <th>真实头像</th>\n",
       "      <th>真实用户</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   日志密度  好友密度  真实头像 真实用户\n",
       "0     0     0     0    N\n",
       "1     0     0     1    Y\n",
       "2     2     2     1    Y\n",
       "3     1     1     1    Y\n",
       "4     2     2     1    Y\n",
       "5     1     1     0    Y\n",
       "6     1     1     1    N\n",
       "7     2     2     1    Y\n",
       "8     1     1     1    Y\n",
       "9     0     0     1    N"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "X['真实用户'] = y\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.8812908992306926)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = X['真实用户']\n",
    "p = s.value_counts()/s.size  ## 各个分类的百比\n",
    "(p*np.log2(1/p)).sum()  ## 计算信息熵"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分裂条件的计算[信息熵]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.8812908992306926)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = X['真实用户'] \n",
    "p = s.value_counts()/s.size \n",
    "# 信息熵[没有根据条件进行划分的信息熵]\n",
    "(p*np.log2(1/p)).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 按照日志密度进行划分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "x =X['日志密度'].unique()\n",
    "x.sort()# 排序从小到大\n",
    "# 如划分呢? 分成两部分\n",
    "users = X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5 0.689659695223976\n",
      "1.5 0.689659695223976\n"
     ]
    }
   ],
   "source": [
    "x =X['日志密度'].unique()\n",
    "x.sort()# 排序从小到大\n",
    "# 如何划分，分成两部分\n",
    "for i in range(len(x)-1):\n",
    "    split = x[i:i+2].mean() # 切2个求 平均值,求平均值[裂分点]\n",
    "    cond = X['日志密度']<split # 小于裂分点的为True\n",
    "    p = cond.value_counts()/len(X) # 计算True的比例\n",
    "    indexes = p.index\n",
    "    entropy = 0\n",
    "    for index in indexes:\n",
    "        user = X[cond == index]['真实用户']\n",
    "        # 分别计算两边的信息熵\n",
    "        p_user = user.value_counts()/len(user)\n",
    "        entropy +=(p_user*np.log2(1/p_user)).sum() *p[index]\n",
    "    print(split,entropy)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 好友密度进行划分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5 0.32451124978365314\n",
      "1.5 0.763547202339972\n"
     ]
    }
   ],
   "source": [
    "x =X['好友密度'].unique()\n",
    "x.sort()# 排序从小到大\n",
    "# 如何划分，分成两部分\n",
    "for i in range(len(x)-1):\n",
    "    split = x[i:i+2].mean() # 切2个求 平均值,求平均值[裂分点]\n",
    "    cond = X['好友密度']<split # 小于裂分点的为True\n",
    "    p = cond.value_counts()/len(X) # 计算True的比例\n",
    "    indexes = p.index\n",
    "    entropy = 0\n",
    "    for index in indexes:\n",
    "        user = X[cond == index]['真实用户']\n",
    "        # 分别计算两边的信息熵\n",
    "        p_user = user.value_counts()/len(user)\n",
    "        entropy +=(p_user*np.log2(1/p_user)).sum() *p[index]\n",
    "    print(split,entropy)\n"
   ]
  }
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